Paper Digest: IJCAI 2019 Highlights

August 8, 2019October 8, 2019 admin

Download IJCAI-2019-Paper-Digests.pdf: one sentence highlight for every single IJCAI-2019 paper (covering Main Track, AI for Improving Human Well-being Track, Understanding Intelligence and Human-level AI in the New Machine Learning era Track). 847 papers in total!

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Agent Based and Multi Agent Systems
Computer Vision
Constraints and SAT
Heuristic Search and Game Playing
Humans and AI
Knowledge Representation and Reasoning
Machine Learning
Machine Learning Applications
Multidisciplinary Topics And Applications
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Planning, Scheduling, Robotics, Uncertainty In AI
AI For Improving Human Well Being
Understanding Intelligence and Human Level AI

International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. In 2019, it is to be held in Macau, China. There were more than 8,000 paper submissions, of which 847 were accepted.

To help AI community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.

We thank all authors for writing these interesting papers, and readers for reading our digests. If you do not want to miss any interesting AI paper, you are welcome to sign up our free paper digest service to get new paper updates customized to your own interests on a daily basis.

Paper Digest Team
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TABLE 1: IJCAI 2019 Papers

	Title	Authors	Highlight
1	Flexible Representative Democracy: An Introduction with Binary Issues	Ben Abramowitz, Nicholas Mattei	We introduce Flexible Representative Democracy (FRD), a novel hybrid of Representative Democracy (RD) and Direct Democracy (DD), in which voters can alter the issue-dependent weights of a set of elected representatives.
2	Portioning Using Ordinal Preferences: Fairness and Efficiency	Stéphane Airiau, Haris Aziz, Ioannis Caragiannis, Justin Kruger, Jérôme Lang, Dominik Peters	We introduce a family of rules for portioning, inspired by positional scoring rules.
3	An Efficient Algorithm for Skeptical Preferred Acceptance in Dynamic Argumentation Frameworks	Gianvincenzo Alfano, Sergio Greco, Francesco Parisi	In this paper we devise an efficient algorithm for computing the skeptical preferred acceptance in dynamic AFs.
4	Strategic Signaling for Selling Information Goods	Shani Alkoby, David Sarne, Igal Milchtaich	This paper studies the benefit in using signaling by an information seller holding information that can completely disambiguate some uncertainty concerning the state of the world for the information buyer.
5	Probabilistic Strategy Logic	Benjamin Aminof, Marta Kwiatkowska, Bastien Maubert, Aniello Murano, Sasha Rubin	We introduce Probabilistic Strategy Logic, an extension of Strategy Logic for stochastic systems.
6	Multi-Agent Pathfinding with Continuous Time	Anton Andreychuk, Konstantin Yakovlev, Dor Atzmon, Roni Stern	In this work, we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions.
7	Weighted Maxmin Fair Share Allocation of Indivisible Chores	Haris Aziz, Hau Chan, Bo Li	We initiate the study of indivisible chore allocation for agents with asymmetric shares.
8	Fair Allocation of Indivisible Goods and Chores	Haris Aziz, Ioannis Caragiannis, Ayumi Igarashi, Toby Walsh	In this paper, we consider a more general scenario where an agent may have negative or positive utility for each item.
9	Strategyproof and Approximately Maxmin Fair Share Allocation of Chores	Haris Aziz, Bo Li, Xiaowei Wu	The fairness concept we consider in this paper is maxmin share (MMS) fairness.
10	Stable and Envy-free Partitions in Hedonic Games	Nathanaël Barrot, Makoto Yokoo	In this paper, we study coalition formation in hedonic games through the fairness criterion of envy-freeness.
11	How Hard Is the Manipulative Design of Scoring Systems?	Dorothea Baumeister, Tobias Hogrebe	We study the computational complexity of two related decision problems.
12	The Price of Fairness for Indivisible Goods	Xiaohui Bei, Xinhang Lu, Pasin Manurangsi, Warut Suksompong	In this paper, we focus instead on notions with guaranteed existence, including envy-freeness up to one good (EF1), balancedness, maximum Nash welfare (MNW), and leximin.
13	Strategy Logic with Simple Goals: Tractable Reasoning about Strategies	Francesco Belardinelli, Wojciech Jamroga, Damian Kurpiewski, Vadim Malvone, Aniello Murano	In this paper we introduce Strategy Logic with simple goals (SL[SG]), a fragment of Strategy Logic that strictly extends the well-known Alternating-time Temporal Logic ATL by introducing arbitrary quantification over the agents’ strategies.
14	Fairness Towards Groups of Agents in the Allocation of Indivisible Items	Nawal Benabbou, Mithun Chakraborty, Edith Elkind, Yair Zick	In this paper, we study the problem of matching a set of items to a set of agents partitioned into types so as to balance fairness towards the types against overall utility/efficiency.
15	Optimality and Nash Stability in Additive Separable Generalized Group Activity Selection Problems	Vittorio Bilò, Angelo Fanelli, Michele Flammini, Gianpiero Monaco, Luca Moscardelli	We consider additively separable GGASPs, where every agent has a separate valuation for each activity as well as for any other agent, and her overall utility is given by the sum of the valuations she has for the selected activity and its participants.
16	An Experimental View on Committees Providing Justified Representation	Robert Bredereck, Piotr Faliszewski, Andrzej Kaczmarczyk, Rolf Niedermeier	We provide an experimental study of committees that achieve (proportional/extended) justified representation (JR/PJR/EJR).
17	A Contribution to the Critique of Liquid Democracy	Ioannis Caragiannis, Evi Micha	We revisit a recent model by Kahng et al. [2018] and conclude with three negative results, criticizing an important assumption of their modeling, as well as liquid democracy more generally.
18	Be a Leader or Become a Follower: The Strategy to Commit to with Multiple Leaders	Matteo Castiglioni, Alberto Marchesi, Nicola Gatti	We study the problem of computing correlated strategies to commit to in games with multiple leaders and followers.
19	On the Problem of Assigning PhD Grants	Katarína Cechlárová, Laurent Gourvès, Julien Lesca	In this paper, we study the problem of assigning PhD grants.
20	Maximin-Aware Allocations of Indivisible Goods	Hau Chan, Jing Chen, Bo Li, Xiaowei Wu	In particular, we propose the maximin aware (MMA) fairness measure, which guarantees that every agent, given the bundle allocated to her, is aware that she does not envy at least one other agent, even if she does not know how the other goods are distributed among other agents.
21	Reachability and Coverage Planning for Connected Agents	Tristan Charrier, Arthur Queffelec, Ocan Sankur, François Schwarzentruber	We establish the complexity of these problems on known classes, and introduce a new class called sight-moveable graphs which admit efficient algorithms.
22	Approximately Maximizing the Broker’s Profit in a Two-sided Market	Jing Chen, Bo Li, Yingkai Li	We study how to maximize the broker’s (expected) profit in a two-sided market, where she buys items from a set of sellers and resells them to a set of buyers.
23	Election with Bribe-Effect Uncertainty: A Dichotomy Result	Lin Chen, Lei Xu, Shouhuai Xu, Zhimin Gao, Weidong Shi	In this paper, we initiate the study of a more realistic model where each voter is associated with a willingness function, rather than a fixed threshold value.
24	Dispatching Through Pricing: Modeling Ride-Sharing and Designing Dynamic Prices	Mengjing Chen, Weiran Shen, Pingzhong Tang, Song Zuo	In this paper, we aim to tackle this problem via an economic approach.
25	ATSIS: Achieving the Ad hoc Teamwork by Sub-task Inference and Selection	Shuo Chen, Ewa Andrejczuk, Athirai A. Irissappane, Jie Zhang	For this reason, we present Ad Hoc Teamwork by Sub-task Inference and Selection (ATSIS) algorithm that uses a sub-task inference without relying on teammates’ models.
26	Network Formation under Random Attack and Probabilistic Spread	Yu Chen, Shahin Jabbari, Michael Kearns, Sanjeev Khanna, Jamie Morgenstern	Our goal is to understand the properties of the equilibrium networks formed in this game.
27	Cap-and-Trade Emissions Regulation: A Strategic Analysis	Frank Cheng, Yagil Engel, Michael P. Wellman	We find that while cap-and-trade results improves efficiency overall, consumers bear a disproportionate share of regulation cost, as firms use credit trading to segment the vehicle market.
28	A Value-based Trust Assessment Model for Multi-agent Systems	Kinzang Chhogyal, Abhaya Nayak, Aditya Ghose, Hoa K. Dam	In this paper, based on the premise that the more values two agents share, the more they should trust one another, we propose a simple approach to trust assessment between agents based on values, taking into account if agents trust cautiously or boldly, and if they depend on others in carrying out a task.
29	Exploiting Social Influence to Control Elections Based on Scoring Rules	Federico Corò, Emilio Cruciani, Gianlorenzo D’Angelo, Stefano Ponziani	We introduce Linear Threshold Ranking, a natural extension of Linear Threshold Model, which models the change of opinions taking into account the amount of exercised influence.
30	Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs	Sankarshan Damle, Moin Hussain Moti, Praphul Chandra, Sujit Gujar	In this work, we present novel mechanisms which break these two barriers, i.e., mechanisms which incorporate negative valuation and asymmetric belief, independently.
31	Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior	Panayiotis Danassis, Aris Filos-Ratsikas, Boi Faltings	We present a novel anytime heuristic (ALMA), inspired by the human principle of altruism, for solving the assignment problem.
32	AsymDPOP: Complete Inference for Asymmetric Distributed Constraint Optimization Problems	Yanchen Deng, Ziyu Chen, Dingding Chen, Wenxin Zhang, Xingqiong Jiang	Rather than disclosing private functions explicitly to facilitate local eliminations, we solve the problem by enforcing delayed eliminations and propose AsymDPOP, the first inference-based complete algorithm for ADCOPs.
33	Preferred Deals in General Environments	Yuan Deng, Sébastien Lahaie, Vahab Mirrokni	We consider the problem of designing preferred deals (inventory, price, quantity) in the presence of general convex constraints, including budget constraints, and propose an approximation algorithm to maximize the revenue obtained from the deals.
34	A Parameterized Perspective on Protecting Elections	Palash Dey, Neeldhara Misra, Swaprava Nath, Garima Shakya	We propose two greedy algorithms for the OPTIMAL DEFENSE problem and empirically show that they perform effectively on reasonable voting profiles.
35	Spotting Collective Behaviour of Online Frauds in Customer Reviews	Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty	Here, we propose DeFrauder, an unsupervised method to detect online fraud reviewer groups.
36	Equilibrium Characterization for Data Acquisition Games	Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael Kearns, Zachary Schutzman	We study a game between two firms which each provide a service based on machine learning.
37	Protecting Elections by Recounting Ballots	Edith Elkind, Jiarui Gan, Svetlana Obraztsova, Zinovi Rabinovich, Alexandros A. Voudouris	In this work, we consider a two-stage voting manipulation scenario.
38	Schelling Games on Graphs	Edith Elkind, Jiarui Gan, Ayumi Igarashi, Warut Suksompong, Alexandros A. Voudouris	We consider strategic games that are inspired by Schelling’s model of residential segregation.
39	Reallocating Multiple Facilities on the Line	Dimitris Fotakis, Loukas Kavouras, Panagiotis Kostopanagiotis, Philip Lazos, Stratis Skoulakis, Nikos Zarifis	Using an LP-based approach, we present a polynomial time algorithm that computes the optimal solution for any number of facilities.
40	Equitable Allocations of Indivisible Goods	Rupert Freeman, Sujoy Sikdar, Rohit Vaish, Lirong Xia	In this work, we study equitable allocations of indivisible goods among agents with additive valuations.
41	Average-case Analysis of the Assignment Problem with Independent Preferences	Yansong Gao, Jie Zhang	In this paper, we offer an affirmative answer to this question by showing that the ratio is bounded by $1/\mu$ when the preference values are independent and identically distributed random variables, where $\mu$ is the expectation of the value distribution.
42	Improving Nash Social Welfare Approximations	Jugal Garg, Peter McGlaughlin	We present novel definitions of fairness concepts in terms of market prices, and design a new scheme to round a market equilibrium into an integral allocation that provides most of the fairness properties of an integral max NSW allocation.
43	On the Efficiency and Equilibria of Rich Ads	MohammadAmin Ghiasi, MohammadTaghi Hajiaghayi, Sébastien Lahaie, Hadi Yami	In this paper, we consider a model where several slots are available on the search results page, as in the classic generalized second-price auction (GSP), but now a bidder can be allocated several consecutive slots, which are interpreted as a rich ad.
44	Identifying vulnerabilities in trust and reputation systems	Taha D. Güne?, Long Tran-Thanh, Timothy J. Norman	We present a novel method for automatically identifying vulnerabilities in such systems by formulating the problem as a derivative-free optimisation problem and applying efficient sampling methods.
45	An Asymptotically Optimal VCG Redistribution Mechanism for the Public Project Problem	Mingyu Guo	We propose an asymptotically optimal mechanism, which achieves a worst-case efficiency ratio of 1, under a minor technical assumption: we assume the agents’ valuations are rational numbers with bounded denominators.
46	On Succinct Encodings for the Tournament Fixing Problem	Sushmita Gupta, Saket Saurabh, Ramanujan Sridharan, Meirav Zehavi	In this paper, we present the first polynomial kernelization for TFP parameterized by the feedback arc set number of the input tournament.
47	On Computational Tractability for Rational Verification	Julian Gutierrez, Muhammad Najib, Giuseppe Perelli, Michael Wooldridge	In this paper we show that the complexity of rational verification can be greatly reduced by restricting specifications to GR(1), a fragment of LTL that can represent most response properties of reactive systems.
48	Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm	John Harwell, Maria Gini	We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process.
49	Achieving a Fairer Future by Changing the Past	Jiafan He, Ariel D. Procaccia, Alexandros Psomas, David Zeng	We study the problem of allocating T indivisible items that arrive online to agents with additive valuations.
50	Compact Representation of Value Function in Partially Observable Stochastic Games	Karel Horák, Branislav Bošanský, Christopher Kiekintveld, Charles Kamhoua	We propose an abstraction technique that addresses this curse of dimensionality by projecting the high-dimensional beliefs onto characteristic vectors of significantly lower dimension (e.g., marginal probabilities).
51	Explicitly Coordinated Policy Iteration	Yujing Hu, Yingfeng Chen, Changjie Fan, Jianye Hao	Based on the necessary conditions of an optimal policy, we propose the explicitly coordinated policy iteration (EXCEL) algorithm which always forces agents to coordinate by comparing the agents’ separated optimistic and average value functions.
52	Robustness against Agent Failure in Hedonic Games	Ayumi Igarashi, Kazunori Ota, Yuko Sakurai, Makoto Yokoo	In this paper we propose a novel criterion that reshapes stability form robustness aspect.
53	The Interplay of Emotions and Norms in Multiagent Systems	Anup K. Kalia, Nirav Ajmeri, Kevin S. Chan, Jin-Hee Cho, Sibel Adal?, Munindar P. Singh	We study how emotions influence norm outcomes in decision-making contexts.
54	An Ordinal Banzhaf Index for Social Ranking	Hossein Khani, Stefano Moretti, Meltem Öztürk	We introduce a new method to rank single elements given an order over their sets.
55	Multigoal Committee Selection	Maciej Kocot, Anna Kolonko, Edith Elkind, Piotr Faliszewski, Nimrod Talmon	We study the problem of computing committees that perform well according to several different criteria, which are expressed as committee scoring rules.
56	Neural Networks for Predicting Human Interactions in Repeated Games	Yoav Kolumbus, Gali Noti	We consider the problem of predicting human players’ actions in repeated strategic interactions.
57	Almost Envy-Freeness in Group Resource Allocation	Maria Kyropoulou, Warut Suksompong, Alexandros A. Voudouris	We study the problem of fairly allocating indivisible goods between groups of agents using the recently introduced relaxations of envy-freeness.
58	A Quantitative Analysis of Multi-Winner Rules	Martin Lackner, Piotr Skowron	We provide a quantitative analysis using methods from the theory of approximation algorithms and estimate how well multi-winner rules approximate two extreme objectives: diversity as captured by the Approval Chamberlin–Courant rule and individual excellence as captured by Multi-winner Approval Voting.
59	Correlating Preferences and Attributes: Nearly Single-Crossing Profiles	Foram Lakhani, Dominik Peters, Edith Elkind	The goal of this paper is to evaluate the computational feasibility of this approach.
60	Automated Negotiation with Gaussian Process-based Utility Models	Haralambie Leahu, Michael Kaisers, Tim Baarslag	We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate it into a (belief) Markov Decision Process paradigm which formalizes automated negotiation processes with incomplete information.
61	Temporal Information Design in Contests	Priel Levy, David Sarne, Yonatan Aumann	We study temporal information design in contests, wherein the organizer may, possibly incrementally, disclose information about the participation and performance of some contestants to other (later) contestants.
62	Diffusion and Auction on Graphs	Bin Li, Dong Hao, Dengji Zhao, Makoto Yokoo	For the first time, we expand the domain of the classic auction to a social graph and formally identify a new class of auction mechanisms on graphs.
63	Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search	Jiaoyang Li, Ariel Felner, Eli Boyarski, Hang Ma, Sven Koenig	In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only.
64	Integrating Decision Sharing with Prediction in Decentralized Planning for Multi-Agent Coordination under Uncertainty	Minglong Li, Wenjing Yang, Zhongxuan Cai, Shaowu Yang, Ji Wang	In this paper, we propose an approach for improving the sharing utilization by integrating information sharing with prediction in decentralized planning.
65	Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns	Yong Liu, Yujing Hu, Yang Gao, Yingfeng Chen, Changjie Fan	In this work, we propose more scalable transfer learning methods based on a novel MDP similarity concept.
66	Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent	Edward Lockhart, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr TImbers, Karl Tuyls	In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents.
67	Computational Aspects of Equilibria in Discrete Preference Games	Phani Raj Lolakapuri, Umang Bhaskar, Ramasuri Narayanam, Gyana R Parija, Pankaj S Dayama	We study the complexity of equilibrium computation in discrete preference games.
68	Multi-Robot Planning Under Uncertain Travel Times and Safety Constraints	Masoumeh Mansouri, Bruno Lacerda, Nick Hawes, Federico Pecora	We present a novel modelling and planning approach for multi-robot systems under uncertain travel times.
69	Leadership in Congestion Games: Multiple User Classes and Non-Singleton Actions	Alberto Marchesi, Matteo Castiglioni, Nicola Gatti	In this paper, we extend the state of the art along two main directions.
70	Graphical One-Sided Markets	Sagar Massand, Sunil Simon	We study the problem of allocating indivisible objects to a set of rational agents where each agent’s final utility depends on the intrinsic valuation of the allocated item as well as the allocation within the agent’s local neighbourhood.
71	Reachability Games in Dynamic Epistemic Logic	Bastien Maubert, Sophie Pinchinat, François Schwarzentruber	We study the problem of existence of a strategy for the controller, which generalises the classic epistemic planning problem, and we solve it for several types of actions such as public announcements and public actions.
72	FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings	Moin Hussain Moti, Dimitris Chatzopoulos, Pan Hui, Sujit Gujar	In this work, we introduce selective and cumulative fairness.
73	Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees	Aadesh Neupane, Michael Goodrich	This paper introduces an algorithm that evolves problem-specific swarm behaviors by combining multi-agent grammatical evolution and Behavior Trees (BTs).
74	A Probabilistic Logic for Resource-Bounded Multi-Agent Systems	Hoang Nga Nguyen, Abdur Rakib	In this paper, we propose a logic for reasoning about coalitional power under resource constraints in the probabilistic setting.
75	Imitative Attacker Deception in Stackelberg Security Games	Thanh Nguyen, Haifeng Xu	We provide a clean characterization about the game equilibrium as well as optimal algorithms to compute the equilibrium.
76	Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding	Keisuke Okumura, Manao Machida, Xavier Défago, Yasumasa Tamura	We present here a novel approach to iterative MAPF, that we call Priority Inheritance with Backtracking (PIBT).
77	Approval-Based Elections and Distortion of Voting Rules	Grzegorz Pierczy?ski, Piotr Skowron	We consider elections where both voters and candidates can be associated with points in a metric space and voters prefer candidates that are closer to those that are farther away.
78	Ad Hoc Teamwork With Behavior Switching Agents	Manish Ravula, Shani Alkoby, Peter Stone	In this work, we relax this assumption and investigate settings in which teammates can change their types during the course of the task.
79	Ridesharing with Driver Location Preferences	Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma, David C. Parkes	We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations.
80	Multi-Population Congestion Games With Incomplete Information	Charlotte Roman, Paolo Turrini	Here we study traffic networks with multiple origin-destination pairs, relaxing the simplifying assumption of agents having complete knowledge of the network structure.
81	Sybil-Resilient Reality-Aware Social Choice	Gal Shahaf, Ehud Shapiro, Nimrod Talmon	Thus, our goal here is to enhance social choice theory with effective group decision mechanisms for communities with bounded sybil penetration.
82	Preferences Single-Peaked on a Tree: Sampling and Tree Recognition	Jakub Sliwinski, Edith Elkind	We consider the setting where voters’ preferences are independently sampled from rankings that are single-peaked on a given tree, and study the problem of reliably identifying the tree that generated the observed votes.
83	Model-Free Model Reconciliation	Sarath Sreedharan, Alberto Olmo Hernandez, Aditya Prasad Mishra, Subbarao Kambhampati	Our goal in this paper is to adapt the model reconciliation approach to a more general planning paradigm and discuss how such methods could be used when user models are no longer explicitly available.
84	Aggregating Incomplete Pairwise Preferences by Weight	Zoi Terzopoulou, Ulle Endriss	We develop a model for the aggregation of preferences that do not need to be either complete or transitive.
85	A Regularized Opponent Model with Maximum Entropy Objective	Zheng Tian, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou, Jun Wang	In this paper, we redefine the binary random variable o in multi-agent setting and formalize multi-agent reinforcement learning (MARL) as probabilistic inference.
86	Exploring the Task Cooperation in Multi-goal Visual Navigation	Yuechen Wu, Zhenhuan Rao, Wei Zhang, Shijian Lu, Weizhi Lu, Zheng-Jun Zha	In this work, we present a model-embedded actor-critic architecture for the multi-goal visual navigation task.
87	On Strategyproof Conference Peer Review	Yichong Xu, Han Zhao, Xiaofei Shi, Nihar B. Shah	In this work, we address this problem through the lens of social choice, and present a theoretical framework for strategyproof and efficient peer review.
88	Towards Efficient Detection and Optimal Response against Sophisticated Opponents	Tianpei Yang, Jianye Hao, Zhaopeng Meng, Chongjie Zhang, Yan Zheng, Ze Zheng	This paper proposes a novel approach called Bayes-ToMoP which can efficiently detect the strategy of opponents using either stationary or higher-level reasoning strategies.
89	Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Deep Reinforcement Learning Framework	Yaodong Yang, Jianye Hao, Yan Zheng, Chao Yu	In this paper, we focus on a microgrid scenario in which modern homes interact together under a large-scale setting to better optimize their electricity cost.
90	Complexity of Manipulating and Controlling Approval-Based Multiwinner Voting	Yongjie Yang	We study the complexity of several manipulation and control problems for six prevalent approval based multiwinner voting rules.
91	On the Tree Representations of Dichotomous Preferences	Yongjie Yang	We study numerous restricted domains of dichotomous preferences with respect to some tree structures.
92	The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control	Chao Yu, Guozhen Tan	This paper investigates a middle ground solution between decentralized interactions and centralized administrations for coordinating agents beyond inefficient behavior.
93	Decentralized Optimization with Edge Sampling	Chi Zhang, Qianxiao Li, Peilin Zhao	In this paper, we propose a decentralized distributed algorithm with stochastic communication among nodes, building on a sampling method called “edge sampling”.
94	Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy	Pengzhan Zhou, Xin Wei, Cong Wang, Yuanyuan Yang	In this paper, we investigate this general problem by considering a system with k levels of difficulty.
95	CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network	Sayan Banerjee, Avik Hati, Subhasis Chaudhuri, Rajbabu Velmurugan	In this paper, we propose a novel deep convolution neural network based end-to-end co-segmentation model.
96	Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition	Bing Cao, Nannan Wang, Xinbo Gao, Jie Li, Zhifeng Li	This paper presents a deep neural network approach namely Multi-Margin based Decorrelation Learning (MMDL) to extract decorrelation representations in a hyperspherical space for cross-domain face images.
97	Generalized Zero-Shot Vehicle Detection in Remote Sensing Imagery via Coarse-to-Fine Framework	Hong Chen, Yongtan Luo, Liujuan Cao, Baochang Zhang, Guodong Guo, Cheng Wang, Jonathan Li, Rongrong Ji	In this paper, we introduce a novel coarse-to-fine framework, which decomposes vehicle detection into segmentation-based vehicle localization and generalized zero-shot vehicle classification. To the best of our knowledge, there is no publically available dataset to test comparative methods, we therefore construct a new dataset to fill this gap of evaluation.
98	Structure-Aware Residual Pyramid Network for Monocular Depth Estimation	Xiaotian Chen, Xuejin Chen, Zheng-Jun Zha	In this paper, we propose a Structure-Aware Residual Pyramid Network (SARPN) to exploit multi-scale structures for accurate depth prediction.
99	A Deep Bi-directional Attention Network for Human Motion Recovery	Qiongjie Cui, Huaijiang Sun, Yupeng Li, Yue Kong	To address these issues, we propose a deep bi-directional attention network (BAN) which can not only capture the long-term dependencies but also adaptively extract relevant information at each time step.
100	On Retrospecting Human Dynamics with Attention	Minjing Dong, Chang Xu	To address these challenges, we propose to retrospect human dynamics with attention.
101	Learning to Draw Text in Natural Images with Conditional Adversarial Networks	Shancheng Fang, Hongtao Xie, Jianjun Chen, Jianlong Tan, Yongdong Zhang	In this work, we propose an entirely learning-based method to automatically synthesize text sequence in natural images leveraging conditional adversarial networks.
102	Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval	Lianli Gao, Xiaosu Zhu, Jingkuan Song, Zhou Zhao, Heng Tao Shen	In this work, we propose a deep progressive quantization (DPQ) model, as an alternative to PQ, for large scale image retrieval.
103	ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs	Amir Gholaminejad, Kurt Keutzer, George Biros	We discuss the underlying problems, and to address them we propose ANODE, a neural ODE framework which avoids the numerical instability related problems noted above.
104	Asynchronous Stochastic Frank-Wolfe Algorithms for Non-Convex Optimization	Bin Gu, Wenhan Xian, Heng Huang	To address this challenging problem, in this paper, we propose our asynchronous stochastic Frank-Wolfe algorithm (AsySFW) and its variance reduction version (AsySVFW) for solving the constrained non-convex optimization problems.
105	Dense Temporal Convolution Network for Sign Language Translation	Dan Guo, Shuo Wang, Qi Tian, Meng Wang	To align the sign language actions and translate them into the respective words automatically, this paper proposes a dense temporal convolution network, termed DenseTCN which captures the actions in hierarchical views.
106	Connectionist Temporal Modeling of Video and Language: a Joint Model for Translation and Sign Labeling	Dan Guo, Shengeng Tang, Meng Wang	This paper proposes a Connectionist Temporal Modeling (CTM) network for sentence translation and sign labeling.
107	3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention	Zhizhong Han, Xiyang Wang, Chi Man Vong, Yu-Shen Liu, Matthias Zwicker, C. L. Philip Chen	We propose 3DViewGraph to resolve this issue, which learns 3D global features by more effectively aggregating unordered views with attention.
108	Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views	Zhizhong Han, Xinhai Liu, Yu-Shen Liu, Matthias Zwicker	In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views.
109	MAT-Net: Medial Axis Transform Network for 3D Object Recognition	Jianwei Hu, Bin Wang, Lihui Qian, Yiling Pan, Xiaohu Guo, Lingjie Liu, Wenping Wang	In this work, we present MAT-Net, a neural network which captures local and global features from the Medial Axis Transform (MAT).
110	Dynamic Feature Fusion for Semantic Edge Detection	Yuan Hu, Yunpeng Chen, Xiang Li, Jiashi Feng	In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively.
111	Multi-Level Visual-Semantic Alignments with Relation-Wise Dual Attention Network for Image and Text Matching	Zhibin Hu, Yongsheng Luo, Jiong Lin, Yan Yan, Jian Chen	In this paper, we propose a relation-wise dual attention network (RDAN) for image-text matching.
112	Learning Unsupervised Visual Grounding Through Semantic Self-Supervision	Syed Ashar Javed, Shreyas Saxena, Vineet Gandhi	In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision.
113	Supervised Set-to-Set Hashing in Visual Recognition	I-Hong Jhuo	In this paper, we consider the fundamental problem of finding a nearest set from a collection of sets, to a query set.
114	Generative Image Inpainting with Submanifold Alignment	Ang Li, Jianzhong Qi, Rui Zhang, Xingjun Ma, Kotagiri Ramamohanarao	To address this limitation, we propose to enforce the alignment (or closeness) between the local data submanifolds (subspaces) around restored images and those around the original (uncorrupted) images during the learning process of GAN-based inpainting models.
115	Detecting Robust Co-Saliency with Recurrent Co-Attention Neural Network	Bo Li, Zhengxing Sun, Lv Tang, Yunhan Sun, Jinlong Shi	This paper proposes a novel deep learning co-saliency detection approach which simultaneously learns single image properties and robust group feature in a recurrent manner.
116	Variation Generalized Feature Learning via Intra-view Variation Adaptation	Jiawei Li, Mang Ye, Andy Jinhua Ma, Pong C Yuen	In this paper, we propose a Variation Generalized Feature Learning (VGFL) method to learn adaptable feature representation with intra-view positives.
117	Pedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation	Qiaozhe Li, Xin Zhao, Ran He, Kaiqi Huang	To achieve effective recognition, this paper presents a graph-based global reasoning framework to jointly model potential visual-semantic relations of attributes and distill auxiliary human parsing knowledge to guide the relational learning.
118	Rethinking Loss Design for Large-scale 3D Shape Retrieval	Zhaoqun Li, Cheng Xu, Biao Leng	In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class.
119	Attribute-Aware Convolutional Neural Networks for Facial Beauty Prediction	Luojun Lin, Lingyu Liang, Lianwen Jin, Weijie Chen	To address this problem, we propose an Attribute-aware Convolutional Neural Network (AaNet) that modulates the filters of the main network, adaptively, using parameter generators that take beauty-related attributes as extra inputs.
120	Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs	Chunlei Liu, Wenrui Ding, Xin Xia, Yuan Hu, Baochang Zhang, Jianzhuang Liu, Bohan Zhuang, Guodong Guo	In this paper, we propose rectified binary convolutional networks (RBCNs), towards optimized BCNNs, by combining full-precision kernels and feature maps to rectify the binarization process in a unified framework.
121	Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion	Dongnan Liu, Donghao Zhang, Yang Song, Chaoyi Zhang, Fan Zhang, Lauren O’Donnell, Weidong Cai	In this work, we propose a panoptic segmentation model which incorporates an auxiliary semantic segmentation branch with the instance branch to integrate global and local features.
122	Densely Connected Attention Flow for Visual Question Answering	Fei Liu, Jing Liu, Zhiwei Fang, Richang Hong, Hanqing Lu	Therefore, in this paper, we propose a novel DCAF (Densely Connected Attention Flow) framework for modeling dense interactions.
123	Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity	Liang Liu, Guangyao Zhai, Wenlong Ye, Yong Liu	In this work, we present a unified framework for joint unsupervised learning of stereo depth and optical flow with explicit local rigidity to estimate scene flow.
124	Resolution-invariant Person Re-Identification	Shunan Mao, Shiliang Zhang, Ming Yang	This paper learns person representations robust to resolution variance through jointly training a Foreground-Focus Super-Resolution (FFSR) module and a Resolution-Invariant Feature Extractor (RIFE) by end-to-end CNN learning.
125	Low Shot Box Correction for Weakly Supervised Object Detection	Tianxiang Pan, Bin Wang, Guiguang Ding, Jungong Han, Junhai Yong	To solve this problem, we define a low-shot weakly supervised object detection task and propose a novel low-shot box correction network to address it.
126	DBDNet: Learning Bi-directional Dynamics for Early Action Prediction	Guoliang Pang, Xionghui Wang, Jian-Fang Hu, Qing Zhang, Wei-Shi Zheng	To obtain a reliable future estimation, a novel encoder-decoder architecture is proposed for integrating the tasks of synthesizing future motions from observed videos and reconstructing observed motions from synthesized future motions in an unified framework, which can capture the bi-directional dynamics depicted in partial videos along the temporal (past-to-future) direction and reverse chronological (future-back-to-past) direction.
127	Deep Light-field-driven Saliency Detection from a Single View	Yongri Piao, Zhengkun Rong, Miao Zhang, Xiao Li, Huchuan Lu	In this paper, we show for the first time that saliency detection problem can be reformulated as two sub-problems: light field synthesis from a single view and light-field-driven saliency detection.
128	Deep Recurrent Quantization for Generating Sequential Binary Codes	Jingkuan Song, Xiaosu Zhu, Lianli Gao, Xin-Shun Xu, Wu Liu, Heng Tao Shen	To address this issue, we propose a Deep Recurrent Quantization (DRQ) architecture which can generate sequential binary codes.
129	Talking Face Generation by Conditional Recurrent Adversarial Network	Yang Song, Jingwen Zhu, Dawei Li, Andy Wang, Hairong Qi	We propose a novel conditional recurrent generation network that incorporates both image and audio features in the recurrent unit for temporal dependency.
130	Hallucinating Optical Flow Features for Video Classification	Yongyi Tang, Lin Ma, Lianqiang Zhou	In this paper, we propose a motion hallucination network, namely MoNet, to imagine the optical flow features from the appearance features, with no reliance on the optical flow computation.
131	Color-Sensitive Person Re-Identification	Guan’an Wang, Yang Yang, Jian Cheng, Jinqiao Wang, Zengguang Hou	In this paper, we propose a novel Color-Sensitive Re-ID to take full advantage of color information.
132	Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation	Jing Wang, Yingwei Pan, Ting Yao, Jinhui Tang, Tao Mei	In this paper, we present a new design — Convolutional Auto-Encoding (CAE) that purely employs convolutional and deconvolutional auto-encoding framework for topic modeling on the region-level features of an image.
133	DSRN: A Deep Scale Relationship Network for Scene Text Detection	Yuxin Wang, Hongtao Xie, Zilong Fu, Yongdong Zhang	To address this problem, we propose an end-to-end architecture called Deep Scale Relationship Network (DSRN) to map multi-scale convolution features onto a scale invariant space to obtain uniform activation of multi-size text instances.
134	Transferable Adversarial Attacks for Image and Video Object Detection	Xingxing Wei, Siyuan Liang, Ning Chen, Xiaochun Cao	To address these issues, we present a generative method to obtain adversarial images and videos, thereby significantly reducing the processing time.
135	Video Interactive Captioning with Human Prompts	Aming Wu, Yahong Han, Yi Yang	In this paper, we make a new attempt that, we launch a round of interaction between a human and a captioning agent.
136	Mutually Reinforced Spatio-Temporal Convolutional Tube for Human Action Recognition	Haoze Wu, Jiawei Liu, Zheng-Jun Zha, Zhenzhong Chen, Xiaoyan Sun	In this work, we propose a novel and efficient Mutually Reinforced Spatio-Temporal Convolutional Tube (MRST) for human action recognition.
137	Densely Supervised Hierarchical Policy-Value Network for Image Paragraph Generation	Siying Wu, Zheng-Jun Zha, Zilei Wang, Houqiang Li, Feng Wu	In this paper, we propose a novel Densely Supervised Hierarchical Policy-Value (DHPV) network for effective paragraph generation.
138	Graph Convolutional Network Hashing for Cross-Modal Retrieval	Ruiqing Xu, Chao Li, Junchi Yan, Cheng Deng, Xianglong Liu	In this paper, we propose a Graph Convolutional Hashing (GCH) approach, which learns modality-unified binary codes via an affinity graph.
139	MSR: Multi-Scale Shape Regression for Scene Text Detection	Chuhui Xue, Shijian Lu, Wei Zhang	This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes.
140	Dynamically Visual Disambiguation of Keyword-based Image Search	Yazhou Yao, Zeren Sun, Fumin Shen, Li Liu, Limin Wang, Fan Zhu, Lizhong Ding, Gangshan Wu, Ling Shao	To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.
141	High Performance Gesture Recognition via Effective and Efficient Temporal Modeling	Yang Yi, Feng Ni, Yuexin Ma, Xinge Zhu, Yuankai Qi, Riming Qiu, Shijie Zhao, Feng Li, Yongtao Wang	In this paper, we focus instead on the 1D convolutional neural networks and propose a simple and efficient architectural unit, Multi-Kernel Temporal Block (MKTB), that models the multi-scale temporal responses by explicitly applying different temporal kernels.
142	Capturing Spatial and Temporal Patterns for Facial Landmark Tracking through Adversarial Learning	Shi Yin, Shangfei Wang, Guozhu Peng, Xiaoping Chen, Bowen Pan	In this paper, we propose a novel deep adversarial framework to explore the shape and temporal dependencies from both appearance level and target label level.
143	Pose-preserving Cross Spectral Face Hallucination	Junchi Yu, Jie Cao, Yi Li, Xiaofei Jia, Ran He	We present an approach to avert the data misalignment problem and faithfully preserve pose, expression and identity information during cross-spectral face hallucination.
144	Generative Visual Dialogue System via Weighted Likelihood Estimation	Heming Zhang, Shalini Ghosh, Larry Heck, Stephen Walsh, Junting Zhang, Jie Zhang, C.-C. Jay Kuo	To address this issue, we propose a novel training scheme in conjunction with weighted likelihood estimation method.
145	Binarized Neural Networks for Resource-Efficient Hashing with Minimizing Quantization Loss	Feng Zheng, Cheng Deng, Heng Huang	In order to solve the problem of memory consumption and computational requirements, this paper proposes a novel learning binary neural network framework to achieve a resource-efficient deep hashing.
146	LRDNN: Local-refining based Deep Neural Network for Person Re-Identification with Attribute Discerning	Qinqin Zhou, Bineng Zhong, Xiangyuan Lan, Gan Sun, Yulun Zhang, Mengran Gou	Since re-ID, pose estimation and attribute recognition are all based on the person appearance information, we propose a Local-refining based Deep Neural Network (LRDNN) to aggregate pose estimation and attribute recognition to improve the re-ID performance.
147	Face Photo-Sketch Synthesis via Knowledge Transfer	Mingrui Zhu, Nannan Wang, Xinbo Gao, Jie Li, Zhifeng Li	Therefore, we propose a novel knowledge transfer framework to synthesize face photos from face sketches or synthesize face sketches from face photos.
148	Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence	Saad Attieh, Nguyen Dang, Christopher Jefferson, Ian Miguel, Peter Nightingale	This paper presents Athanor, a novel local search solver that operates on abstract constraint specifications of combinatorial problems in the Essence language.
149	Constraint Programming for Mining Borders of Frequent Itemsets	Mohamed-Bachir Belaid, Christian Bessiere, Nadjib Lazaar	We propose a generic framework based on constraint programming to mine both borders of frequent itemsets.One can easily decide which border to mine by setting a simple parameter.
150	How to Tame Your Anticipatory Algorithm	Allegra De Filippo, Michele Lombardi, Michela Milano	Given an arbitrary anticipatory algorithm, we present three methods that allow to retain its solution quality at a fraction of the online computational cost, via a substantial degree of offline preparation.
151	Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions	Emir Demirovic, Peter J. Stuckey, James Bailey, Jeffrey Chan, Christopher Leckie, Kotagiri Ramamohanarao, Tias Guns	Our contributions are two-fold: 1) we provide theoretical insight into the properties and computational complexity of predict+optimise problems in general, and 2) develop a novel framework that, in contrast to related work, guarantees to compute the optimal parameters for a linear learning function given any ranking optimisation problem.
152	Privacy-Preserving Obfuscation of Critical Infrastructure Networks	Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck	The paper studies how to release data about a critical infrastructure network (e.g., a power network or a transportation network) without disclosing sensitive information that can be exploited by malevolent agents, while preserving the realism of the network.
153	Solving the Satisfiability Problem of Modal Logic S5 Guided by Graph Coloring	Pei Huang, Minghao Liu, Ping Wang, Wenhui Zhang, Feifei Ma, Jian Zhang	In this paper, we present a novel SAT-based approach for S5 satisfiability problem.
154	DoubleLex Revisited and Beyond	Xuming Huang, Jimmy Lee	The paper proposes Maximum Residue (MR) as a notion to evaluate the strength of a symmetry breaking method.
155	Model-Based Diagnosis with Multiple Observations	Alexey Ignatiev, Antonio Morgado, Georg Weissenbacher, Joao Marques-Silva	The paper proposes not only solutions to correct existing algorithms, but also conditions for improving their run times.
156	Enumerating Potential Maximal Cliques via SAT and ASP	Tuukka Korhonen, Jeremias Berg, Matti Järvisalo	We propose the use of declarative solvers for PMC enumeration as a substitute for the specialized PMC enumeration algorithms employed in current BT implementations.
157	Entropy-Penalized Semidefinite Programming	Mikhail Krechetov, Jakub Marecek, Yury Maximov, Martin Takac	In this paper, we propose Entropy-Penalized Semi-Definite Programming (EP-SDP), which provides a unified framework for a broad class of penalty functions used in practice to promote a low-rank solution.
158	Acquiring Integer Programs from Data	Mohit Kumar, Stefano Teso, Luc De Raedt	We propose ARNOLD, an approach that partially automates the modelling step by learning an integer program from example solutions.
159	Stochastic Constraint Propagation for Mining Probabilistic Networks	Anna Louise D. Latour, Behrouz Babaki, Siegfried Nijssen	For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint.
160	Optimizing Constraint Solving via Dynamic Programming	Shu Lin, Na Meng, Wenxin Li	In this paper we present a different approach–DPSolver–which uses dynamic programming (DP) to efficiently solve certain types of constraint optimization problems (COPs).
161	Constraint-Based Scheduling with Complex Setup Operations: An Iterative Two-Layer Approach	Adriana Pacheco, Cédric Pralet, Stéphanie Roussel	In this paper, we consider scheduling problems involving resources that must perform complex setup operations between the tasks they realize.
162	Phase Transition Behavior of Cardinality and XOR Constraints	Yash Pote, Saurabh Joshi, Kuldeep S. Meel	In this paper, we present the first rigorous empirical study to characterize the runtime behavior of 1-CARD-XOR formulas.
163	GANAK: A Scalable Probabilistic Exact Model Counter	Shubham Sharma, Subhajit Roy, Mate Soos, Kuldeep S. Meel	In this paper, we revisit the architecture of the state-of-the-art dynamic decomposition-based #SAT tool, sharpSAT, and demonstrate that by introducing a new notion of probabilistic component caching and the usage of universal hashing for exact model counting along with the development of several new heuristics can lead to significant performance improvement over state-of-the-art model-counters.
164	Unifying Search-based and Compilation-based Approaches to Multi-agent Path Finding through Satisfiability Modulo Theories	Pavel Surynek	Our novel algorithm called SMT-CBS hence does not branch at the high-level but incrementally extends the propositional model.
165	Integrating Pseudo-Boolean Constraint Reasoning in Multi-Objective Evolutionary Algorithms	Miguel Terra-Neves, Inês Lynce, Vasco Manquinho	In this paper, we propose the integration of constraint-based procedures in evolutionary algorithms for solving MOCO.
166	Resolution and Domination: An Improved Exact MaxSAT Algorithm	Chao Xu, Wenjun Li, Yongjie Yang, Jianer Chen, Jianxin Wang	Particularly, we derive a branching algorithm of running time O*(1.2989^m) for the MaxSAT problem, where m denotes the number of clauses in the given CNF formula.
167	Path Planning with CPD Heuristics	Massimo Bono, Alfonso E. Gerevini, Daniel D. Harabor, Peter J. Stuckey	In this work we investigate CPDs as admissible heuristic functions and we apply them in two distinct settings: problems where the graph is subject to dynamically changing costs, and anytime settings where deliberation time is limited.
168	A+IDA: A Simple Hybrid Search Algorithm	Zhaoxing Bu, Richard E. Korf	We present a simple combination of A* and IDA, which we call A+IDA*.
169	Deanonymizing Social Networks Using Structural Information	Ioannis Caragiannis, Evanthia Tsitsoka	We present two algorithms that attack the problem by exploiting only the structure of the two graphs.
170	Conditions for Avoiding Node Re-expansions in Bounded Suboptimal Search	Jingwei Chen, Nathan R. Sturtevant	This paper explores the properties of priority functions that can find bounded suboptimal solution without requiring node re-expansions.
171	An Efficient Evolutionary Algorithm for Minimum Cost Submodular Cover	Victoria G. Crawford	In this paper, the Minimum Cost Submodular Cover problem is studied, which is to minimize a modular cost function such that the monotone submodular benefit function is above a threshold.
172	An Evolution Strategy with Progressive Episode Lengths for Playing Games	Lior Fuks, Noor Awad, Frank Hutter, Marius Lindauer	In this work, we introduce Progressive Episode Lengths (PEL) as a new technique and incorporate it with ES.
173	Regarding Jump Point Search and Subgoal Graphs	Daniel D. Harabor, Tansel Uras, Peter J. Stuckey, Sven Koenig	In this paper, we define Jump Point Graphs (JP), a preprocessing-based path-planning technique similar to Subgoal Graphs (SG).
174	Iterative Budgeted Exponential Search	Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant	We describe a new algorithmic framework that iteratively controls an expansion budget and solution cost limit, giving rise to new graph and tree search algorithms for which the number of expansions is O(n log C), where C is the optimal solution cost.
175	Direction-Optimizing Breadth-First Search with External Memory Storage	Shuli Hu, Nathan R. Sturtevant	This paper shows how to modify direction-optimizing breadth-first search to build external-memory heuristics.
176	DeltaDou: Expert-level Doudizhu AI through Self-play	Qiqi Jiang, Kuangzheng Li, Boyao Du, Hao Chen, Hai Fang	In this paper, we present a Doudizhu AI by applying deep reinforcement learning from games of self-play.
177	Graph Mining Meets Crowdsourcing: Extracting Experts for Answer Aggregation	Yasushi Kawase, Yuko Kuroki, Atsushi Miyauchi	In this study, we introduce the notion of “expert core”, which is a set of workers that is very unlikely to contain a non-expert.
178	Depth-First Memory-Limited AND/OR Search and Unsolvability in Cyclic Search Spaces	Akihiro Kishimoto, Adi Botea, Radu Marinescu	We give a new theoretical analysis under relaxed assumptions where previous results no longer hold.
179	Branch-and-Cut-and-Price for Multi-Agent Pathfinding	Edward Lam, Pierre Le Bodic, Daniel D. Harabor, Peter J. Stuckey	In this work, we present an optimal algorithm, BCP, that hybridizes both approaches using Branch-and-Cut-and-Price, a decomposition framework developed for mathematical optimization.
180	Local Search with Efficient Automatic Configuration for Minimum Vertex Cover	Chuan Luo, Holger H. Hoos, Shaowei Cai, Qingwei Lin, Hongyu Zhang, Dongmei Zhang	In this work, we present a new local search framework for MinVC called MetaVC, which is highly parametric and incorporates many effective local search techniques.
181	Learning Deep Decentralized Policy Network by Collective Rewards for Real-Time Combat Game	Peixi Peng, Junliang Xing, Lili Cao, Lisen Mu, Chang Huang	To train DDPN effectively, a novel two-stage learning algorithm is proposed which combines imitation learning from opponent and reinforcement learning by no-regret dynamics.
182	Heuristic Search for Homology Localization Problem and Its Application in Cardiac Trabeculae Reconstruction	Xudong Zhang, Pengxiang Wu, Changhe Yuan, Yusu Wang, Dimitris Metaxas, Chao Chen	In this work, we formulate the problem as a heuristic search problem, and propose novel heuristic functions based on advanced topological techniques.
183	Non-smooth Optimization over Stiefel Manifolds with Applications to Dimensionality Reduction and Graph Clustering	Fariba Zohrizadeh, Mohsen Kheirandishfard, Farhad Kamangar, Ramtin Madani	This paper is concerned with the class of non-convex optimization problems with orthogonality constraints.
184	Explaining Reinforcement Learning to Mere Mortals: An Empirical Study	Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett	We present a user study to investigate the impact of explanations on non-experts?
185	Balancing Explicability and Explanations in Human-Aware Planning	Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati	In this paper, we bring these two concepts together and show how an agent can achieve a trade-off between these two competing characteristics of a plan.
186	Multi-agent Attentional Activity Recognition	Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, Zhiwen Yu	In this work, we consider two inherent characteristics of human activities, the spatially-temporally varying salience of features and the relations between activities and corresponding body part motions.
187	Deep Adversarial Social Recommendation	Wenqi Fan, Tyler Derr, Yao Ma, Jianping Wang, Jiliang Tang, Qing Li	In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO.
188	A Semantics-based Model for Predicting Children’s Vocabulary	Ishaan Grover, Hae Won Park, Cynthia Breazeal	In this paper, we present a model that uses word semantics (semantics-based model) to make inferences about a child’s vocabulary from partial information about their existing vocabulary knowledge.
189	STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks	Pengjie Gu, Rong Xiao, Gang Pan, Huajin Tang	To address this issue, we propose a novel spatio-temporal credit assignment algorithm called STCA for training deep spiking neural networks (DSNNs).
190	Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation	Guibing Guo, Shichang Ouyang, Xiaodong He, Fajie Yuan, Xiaohua Liu	To resolve these issues, in this paper we propose two enhancing building blocks for sequential recommendation.
191	Discrete Trust-aware Matrix Factorization for Fast Recommendation	Guibing Guo, Enneng Yang, Li Shen, Xiaochun Yang, Xiaodong He	In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation.
192	Decoding EEG by Visual-guided Deep Neural Networks	Zhicheng Jiao, Haoxuan You, Fan Yang, Xin Li, Han Zhang, Dinggang Shen	Inspired by the success of deep learning on image representation and neural decoding, we proposed a visual-guided EEG decoding method that contains a decoding stage and a generation stage.
193	MiSC: Mixed Strategies Crowdsourcing	Ching Yun Ko, Rui Lin, Shu Li, Ngai Wong	In this work, we propose MiSC ( Mixed Strategies Crowdsourcing), a versatile framework integrating arbitrary conventional crowdsourcing and tensor completion techniques.
194	Exploring Computational User Models for Agent Policy Summarization	Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir	In this paper, we explore the use of different models for extracting summaries.
195	Minimizing Time-to-Rank: A Learning and Recommendation Approach	Haoming Li, Sujoy Sikdar, Rohit Vaish, Junming Wang, Lirong Xia, Chaonan Ye	We develop the first optimization framework to address this problem, and make theoretical as well as practical contributions.
196	DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks	Marco Maier, Daniel Elsner, Chadly Marouane, Meike Zehnle, Christoph Fuchs	In this work, we present our findings towards estimating a user’s flow state based on physiological signals measured using wearable devices.
197	Why Can’t You Do That HAL? Explaining Unsolvability of Planning Tasks	Sarath Sreedharan, Siddharth Srivastava, David Smith, Subbarao Kambhampati	In this paper, we show that hierarchical abstractions can be used to efficiently generate reasons for unsolvability of planning problems.
198	Personalized Multimedia Item and Key Frame Recommendation	Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang	In this paper, we study the general problem of joint multimedia item and key frame recommendation in the absence of the fine-grained user-image behavior.
199	Counterfactual Fairness: Unidentification, Bound and Algorithm	Yongkai Wu, Lu Zhang, Xintao Wu	In this paper, we address this limitation by mathematically bounding the unidentifiable counterfactual quantity, and develop a theoretically sound algorithm for constructing counterfactually fair classifiers.
200	Fast and Accurate Classification with a Multi-Spike Learning Algorithm for Spiking Neurons	Rong Xiao, Qiang Yu, Rui Yan, Huajin Tang	To address these limitations, we propose a simple and effective multi-spike learning rule to train neurons to match their output spike number with a desired one.
201	Achieving Causal Fairness through Generative Adversarial Networks	Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu	In this paper, we investigate the problem of building causal fairness-aware generative adversarial networks (CFGAN), which can learn a close distribution from a given dataset, while also ensuring various causal fairness criteria based on a given causal graph.
202	DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation	Huan Yan, Xiangning Chen, Chen Gao, Yong Li, Depeng Jin	In this paper, we investigate the user viewing behavior in multiple sites based on a large scale real dataset.
203	An Input-aware Factorization Machine for Sparse Prediction	Yantao Yu, Zhen Wang, Bo Yuan	In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features.
204	Multiple Noisy Label Distribution Propagation for Crowdsourcing	Hao Zhang, Liangxiao Jiang, Wenqiang Xu	To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this study.
205	FAHT: An Adaptive Fairness-aware Decision Tree Classifier	Wenbin Zhang, Eirini Ntoutsi	In this paper, we introduce a learning mechanism to design a fair classifier for online stream based decision-making.
206	ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions	Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer, Mark Junjie Li	In this paper, we study weakenings of Faithfulness for constraint-based discovery of semi-Markovian causal models, which accommodate the possibility of latent variables, and show that both (1) and (2) remain the case in this more realistic setting.
207	On the Integration of CP-nets in ASPRIN	Mario Alviano, Javier Romero, Torsten Schaub	In general, we extend ASPRIN with a preference program for CP-nets in order to compute most preferred answer sets via an iterative algorithm.For the specific case of acyclic CP-nets, we provide an approximation by partially ordered set preferences, which are in turn normalized by ASPRIN to take advantage of several highly optimized algorithms implemented by ASP solvers for computing optimal solutions.Finally, we take advantage of a linear-time computable function to address dominance testing for tree-shaped CP-nets.
208	Compilation of Logical Arguments	Leila Amgoud, Dragan Doder	We show that they may miss intuitive consequences, and discuss two sources of this drawback: the definition of logical argument i) may prevent formulas from being justified, and ii) may allow irrelevant information in argument’s support.
209	Observations on Darwiche and Pearl’s Approach for Iterated Belief Revision	Theofanis Aravanis, Pavlos Peppas, Mary-Anne Williams	In this article, we make further observations on the DP approach.
210	Do You Need Infinite Time?	Alessandro Artale, Andrea Mazzullo, Ana Ozaki	In this paper, we investigate first-order temporal logic over finite traces, lifting some known results to a more expressive setting.
211	Stratified Evidence Logics	Philippe Balbiani, David Fernández-Duque, Andreas Herzig, Emiliano Lorini	In this paper we propose an extended framework which allows one to explicitly quantify either the number of evidence sets, or effort, needed to justify a given proposition, provide a complete deductive calculus and a proof of decidability, and show how existing frameworks can be embedded into ours.
212	Worst-Case Optimal Querying of Very Expressive Description Logics with Path Expressions and Succinct Counting	Bartosz Bednarczyk, Sebastian Rudolph	We show that this assumption can be dropped without an increase in complexity and EXPTIME-completeness can be achieved when bounding the number of query atoms, using a novel reduction from query entailment to knowledge base satisfiability.
213	Comparing Options with Argument Schemes Powered by Cancellation	Khaled Belahcene, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, Wassila Ouerdane	We introduce a way of reasoning about preferences represented as pairwise comparative statements, based on a very simple yet appealing principle: cancelling out common values across statements.
214	Possibilistic Games with Incomplete Information	Nahla Ben Amor, Helene Fargier, Régis Sabbadin, Meriem Trabelsi	This paper proposes a representation framework for ordinal games under possibilistic incomplete information (?-games) and extends the fundamental notion of Nash equilibrium (NE) to this framework.
215	Reasoning about Disclosure in Data Integration in the Presence of Source Constraints	Michael Benedikt, Pierre Bourhis, Louis Jachiet, Michaël Thomazo	We study the problem of determining whether a given data integration system discloses a source query to an attacker in the presence of constraints, providing both lower and upper bounds on source-aware disclosure analysis.
216	Mixed-World Reasoning with Existential Rules under Active-Domain Semantics	Meghyn Bienvenu, Pierre Bourhis	In this paper, we study reasoning with existential rules in a setting where some of the predicates may be closed (i.e., their content is fully specified by the data instance) and the remaining open predicates are interpreted under active-domain semantics.
217	Guarantees for Sound Abstractions for Generalized Planning	Blai Bonet, Raquel Fuentetaja, Yolanda E-Martín, Daniel Borrajo	In this work we address this limitation by performing an analysis of the abstraction with respect to the collection, and show how to obtain formal guarantees for generalization.
218	Ontology Approximation in Horn Description Logics	Anneke Bötcher, Carsten Lutz, Frank Wolter	In this paper, we show how to construct complete approximations.
219	Oblivious and Semi-Oblivious Boundedness for Existential Rules	Pierre Bourhis, Michel Leclère, Marie-Laure Mugnier, Sophie Tison, Federico Ulliana, Lily Gallois	We study the notion of boundedness in the context positive existential rules, that is, wether there exists an upper bound to the depth of the chase procedure, that is independent from the initial instance.
220	Reasoning about Quality and Fuzziness of Strategic Behaviours	Patricia Bouyer, Orna Kupferman, Nicolas Markey, Bastien Maubert, Aniello Murano, Giuseppe Perelli	We introduce and study SL[F], a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours.
221	The Complexity of Model Checking Knowledge and Time	Laura Bozzelli, Bastien Maubert, Aniello Murano	We establish the precise complexity of the model checking problem for the main logics of knowledge and time.
222	Planning for LTLf /LDLf Goals in Non-Markovian Fully Observable Nondeterministic Domains	Ronen I. Brafman, Giuseppe De Giacomo	In this paper, we investigate non-Markovian Nondeterministic Fully Observable Planning Domains (NMFONDs), variants of Nondeterministic Fully Observable Planning Domains (FONDs) where the next state is determined by the full history leading to the current state.
223	Causal Discovery with Cascade Nonlinear Additive Noise Model	Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao	In this work, we propose a cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.
224	Enriching Ontology-based Data Access with Provenance	Diego Calvanese, Davide Lanti, Ana Ozaki, Rafael Penaloza, Guohui Xiao	We address this challenge by enriching OBDA with provenance semirings, taking inspiration from database theory.
225	Chasing Sets: How to Use Existential Rules for Expressive Reasoning	David Carral, Irina Dragoste, Markus Krötzsch, Christian Lewe	We propose that modern existential rule reasoners can enable fully declarative implementations of rule-based inference methods in knowledge representation, in the sense that a particular calculus is captured by a fixed set of rules that can be evaluated on varying inputs (encoded as facts).
226	Simple Conditionals with Constrained Right Weakening	Giovanni Casini, Thomas Meyer, Ivan Varzinczak	In this paper we introduce and investigate a very basic semantics for conditionals that can be used to define a broad class of conditional reasoning.
227	Explanations for Query Answers under Existential Rules	?smail ?lkan Ceylan, Thomas Lukasiewicz, Enrico Malizia, Andrius Vaicenavi?ius	In this paper, we close this gap, and study the problem of explaining query answers in terms of minimal subsets of database facts.
228	Semantic Characterization of Data Services through Ontologies	Gianluca Cima, Maurizio Lenzerini, Antonella Poggi	We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an s-to-o rewriting of a given data service), and computation (computing an s-to-o rewriting of a data service).
229	Measuring the Likelihood of Numerical Constraints	Marco Console, Matthias Hofer, Leonid Libkin	Our goal is to measure the likelihood of the satisfaction of numerical constraints in the absence of prior information.
230	From Statistical Transportability to Estimating the Effect of Stochastic Interventions	Juan D. Correa, Elias Bareinboim	In this paper, we study these violations through causal lens using the formalism of statistical transportability [Pearl and Bareinboim, 2011] (PB, for short).
231	Answer Set Programming for Judgment Aggregation	Ronald de Haan, Marija Slavkovik	We take advantage of this and propose a natural and modular encoding of various judgment aggregation procedures and related problems in JA into ASP.
232	An ASP Approach to Generate Minimal Countermodels in Intuitionistic Propositional Logic	Camillo Fiorentini	We present a procedure to generate minimal models in the number of worlds relying on Answer Set Programming (ASP).
233	Learning Description Logic Concepts: When can Positive and Negative Examples be Separated?	Maurice Funk, Jean Christoph Jung, Carsten Lutz, Hadrien Pulcini, Frank Wolter	We study the fundamental question of when a separating DL concept exists and provide useful model-theoretic characterizations as well as complexity results for the associated decision problem.
234	Aggressive Driving Saves More Time? Multi-task Learning for Customized Travel Time Estimation	Ruipeng Gao, Xiaoyu Guo, Fuyong Sun, Lin Dai, Jiayan Zhu, Chenxi Hu, Haibo Li	In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS traces, smartphone inertial data, and road network within a deep recurrent neural network.
235	Approximating Integer Solution Counting via Space Quantification for Linear Constraints	Cunjing Ge, Feifei Ma, Xutong Ma, Fan Zhang, Pei Huang, Jian Zhang	In this paper, we present and prove a bound of such error for LCs.
236	Best Answers over Incomplete Data : Complexity and First-Order Rewritings	Amélie Gheerbrant, Cristina Sirangelo	We compare different ways of casting query answering as a decision problem and characterise its complexity for first-order queries, showing significant differences in the behavior of best and certain answers.We then restrict attention to best answers for unions of conjunctive queries and produce a practical algorithm for finding them based on query rewriting techniques.
237	On Division Versus Saturation in Pseudo-Boolean Solving	Stephan Gocht, Jakob Nordström, Amir Yehudayoff	We show that PB solvers with division instead of saturation can be exponentially stronger.
238	On Finite and Unrestricted Query Entailment beyond SQ with Number Restrictions on Transitive Roles	Tomasz Gogacz, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García, Jean Christoph Jung, Filip Murlak	We study the description logic SQ with number restrictions applicable to transitive roles, extended with either nominals or inverse roles.
239	Belief Revision Operators with Varying Attitudes Towards Initial Beliefs	Adrian Haret, Stefan Woltran	In this work we look at operators that can assume different attitudes towards original beliefs.
240	Some Things are Easier for the Dumb and the Bright Ones (Beware the Average!)	Wojciech Jamroga, Micha? Knapik	In this paper, we look at the verification of models with “extreme” epistemic structure, and identify several special cases for which model checking is easier than in general.
241	Converging on Common Knowledge	Dominik Klein, Rasmus Kræmmer Rendsvig	Focusing on the coordinated attack problem modeled using dynamic epistemic logic, this paper discusses unreliable communication protocols from a topological perspective and asks “If the generals may communicate indefinitely, will they then converge to a state of common knowledge?”
242	Rational Inference Relations from Maximal Consistent Subsets Selection	Sébastien Konieczny, Pierre Marquis, Srdjan Vesic	In this paper we point out new such relations based on selection of some of the maximal consistent subsets, leading thus to inference relations with a stronger inferential power.
243	How to Handle Missing Values in Multi-Criteria Decision Aiding?	Christophe Labreuche, Sébastien Destercke	Given a model that has been elicited on the complete set of attributes, we are looking thus for a way — called restriction operator — to automatically remove the missing attributes from this model.
244	A Tractable, Expressive, and Eventually Complete First-Order Logic of Limited Belief	Gerhard Lakemeyer, Hector J. Levesque	In this paper, we propose a novel logic of limited belief, which has all three desired properties.
245	Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach	Wuwei Lan, Yanyan Xu, Bin Zhao	Thus, this paper proposes an end-to-end multi-task deep neural model, named Deep Image to Time (DeepI2T), to learn the travel time mainly from the built environment images, a.k.a. the morphological layout images, and showoff the new state-of-the-art performance on real-world datasets in two cities.
246	Augmenting Transfer Learning with Semantic Reasoning	Freddy Lécué, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen	We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings.
247	Revisiting Controlled Query Evaluation in Description Logics	Domenico Lembo, Riccardo Rosati, Domenico Fabio Savo	In this paper we instead consider query answering over all possible optimal censors.
248	Unit Selection Based on Counterfactual Logic	Ang Li, Judea Pearl	Unlike previous works on this problem, which rely on ad-hoc heuristics, we approach this problem formally, using counterfactual logic, to properly capture the nature of the desired behavior.
249	Story Ending Prediction by Transferable BERT	Zhongyang Li, Xiao Ding, Ting Liu	In this study, we investigate a transferable BERT (TransBERT) training framework, which can transfer not only general language knowledge from large-scale unlabeled data but also specific kinds of knowledge from various semantically related supervised tasks, for a target task.
250	Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism	Wei Liu, Zhi-Jie Wang, Bin Yao, Jian Yin	To alleviate these issues, we propose a geographical information based adversarial learning model (Geo-ALM), which can be viewed as a fusion of geographic features and generative adversarial networks.
251	Automatic Verification of FSA Strategies via Counterexample-Guided Local Search for Invariants	Kailun Luo, Yongmei Liu	In this paper, we consider the representation of general strategies that solve a class of (possibly infinitely many) games with similar structures, and their automatic verification, which is an undecidable problem.
252	BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains	Hugo Martin, Patrice Perny	We study the biOWA model for preference aggregation and multicriteria decision making from bipolar rating scales.
253	Satisfaction and Implication of Integrity Constraints in Ontology-based Data Access	Charalampos Nikolaou, Bernardo Cuenca Grau, Egor V. Kostylev, Mark Kaminski, Ian Horrocks	We extend ontology-based data access with integrity constraints over both the source and target schemas.
254	Monitoring of a Dynamic System Based on Autoencoders	Aomar Osmani, Massinissa Hamidi, Salah Bouhouche	We propose in this paper an autoencoder model-based approach for tracking abnormalities in industrial application.
255	Boosting for Comparison-Based Learning	Michael Perrot, Ulrike von Luxburg	We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form “object A is closer to object B than to object C.” In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet comparisons.
256	Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics	Vladislav Ryzhikov, Przemyslaw Andrzej Walega, Michael Zakharyaschev	We investigate the data complexity of answering queries mediated by metric temporal logic ontologies under the event-based semantics assuming that data instances are finite timed words timestamped with binary fractions.
257	Belief Update without Compactness in Non-finitary Languages	Jandson S Ribeiro, Abhaya Nayak, Renata Wassermann	We explore the reason behind this, and subsequently provide an alternative constructive accounts of belief update which is characterised by the full set of KM postulates in this more general framework.
258	What Has Been Said? Identifying the Change Formula in a Belief Revision Scenario	Nicolas Schwind, Katsumi Inoue, Sébastien Konieczny, Jean-Marie Lagniez, Pierre Marquis	We present some sufficient conditions for it, identify its computational complexity, and report the results of some experiments about it.
259	Estimating Causal Effects of Tone in Online Debates	Dhanya Sridhar, Lise Getoor	In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses.
260	Out of Sight But Not Out of Mind: An Answer Set Programming Based Online Abduction Framework for Visual Sensemaking in Autonomous Driving	Jakob Suchan, Mehul Bhatt, Srikrishna Varadarajan	We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking (in the backdrop of autonomous driving).
261	DatalogMTL: Computational Complexity and Expressive Power	Przemys?aw A. Wa??ga, Bernardo Cuenca Grau, Mark Kaminski, Egor V. Kostylev	We study the complexity and expressive power of DatalogMTL – a knowledge representation language that extends Datalog with operators from metric temporal logic (MTL) and which has found applications in ontology-based data access and stream reasoning.
262	Cross-City Transfer Learning for Deep Spatio-Temporal Prediction	Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, Qiang Yang	To address the problem, we propose a novel cross-city transfer learning method for deep spatio-temporal prediction tasks, called RegionTrans.
263	A Modal Characterization Theorem for a Probabilistic Fuzzy Description Logic	Paul Wild, Lutz Schröder, Dirk Pattinson, Barbara König	In the present paper, we provide a characterization of the expressive power of this logic based on this observation: We prove a probabilistic analogue of the classical van Benthem theorem, which states that modal logic is precisely the bisimulation-invariant fragment of first-order logic.
264	Graph WaveNet for Deep Spatial-Temporal Graph Modeling	Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang	To overcome these limitations, we propose in this paper a novel graph neural network architecture, {Graph WaveNet}, for spatial-temporal graph modeling.
265	Profit-driven Task Assignment in Spatial Crowdsourcing	Jinfu Xia, Yan Zhao, Guanfeng Liu, Jiajie Xu, Min Zhang, Kai Zheng	To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform.
266	Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns	Zhipeng Xie, Feiteng Mu	To solve this problem, this paper proposes a method to boost causal embeddings by exploring potential verb-mediated causal patterns.
267	Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning	Bingbing Xu, Huawei Shen, Qi Cao, Keting Cen, Xueqi Cheng	In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph.
268	TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics	Shihui Yang, Jidong Tian, Honglun Zhang, Junchi Yan, Hao He, Yaohui Jin	In this paper, we propose a novel knowledge graph embedding method named TransMS, which translates and transmits multidirectional semantics: i) the semantics of head/tail entities and relations to tail/head entities with nonlinear functions and ii) the semantics from entities to relations with linear bias vectors.
269	Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs	Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo	In this paper, we incorporate neighborhood subgraph-level information of entities, and propose a neighborhood-aware attentional representation method NAEA for multilingual knowledge graphs.
270	The Expected-Length Model of Options	David Abel, John Winder, Marie desJardins, Michael Littman	This paper introduces and motivates the Expected-Length Model (ELM) for options, an alternate model for transition dynamics.
271	Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets	Homayun Afrabandpey, Tomi Peltola, Samuel Kaski	In contrast, we propose eliciting expert knowledge about pairwise feature similarities, to borrow statistical strength in the predictions, and using sequential decision making techniques to minimize the effort of the expert.
272	Inter-node Hellinger Distance based Decision Tree	Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib	This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees.
273	Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts	Hamoon Azizsoltani, Yeo Jin Kim, Markel Sanz Ausin, Tiffany Barnes, Min Chi	We address the credit assignment problem by proposing a Gaussian Process (GP)-based immediate reward approximation algorithm and evaluate its effectiveness in 4 contexts where rewards can be delayed for long trajectories.
274	STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting	Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, Quan Z. Sheng	In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously.
275	Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity	Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang	We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity.
276	Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis	Yogesh Balaji, Martin Renqiang Min, Bing Bai, Rama Chellappa, Hans Peter Graf	In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a conditional GAN model with a novel multi-scale text-conditioning scheme that improves text-video associations. In addition, we construct a synthetic dataset of text-conditioned moving shapes to systematically evaluate our conditioning scheme.
277	An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments	Elaheh Barati, Xuewen Chen	In this paper, we propose a deep reinforcement learning method and an attention mechanism in a multi-view environment.
278	Motion Invariance in Visual Environments	Alessandro Betti, Marco Gori, Stefano Melacci	In this paper, we claim that the processing of a stream of frames naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of visual learning based on convolutional features.
279	Optimal Exploitation of Clustering and History Information in Multi-armed Bandit	Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistuba	We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms.
280	Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression	Nadjet Bourdache, Patrice Perny, Olivier Spanjaard	We introduce a new model-based incremental choice procedure for multicriteria decision support, that interleaves the analysis of the set of alternatives and the elicitation of weighting coefficients that specify the role of criteria in rank-dependent models such as ordered weighted averages (OWA) and Choquet integrals.
281	A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees	Klaus Broelemann, Gjergji Kasneci	We propose shallow model trees as a way to combine simple and highly transparent predictive models for higher predictive power without losing the transparency of the original models.
282	Matrix Completion in the Unit Hypercube via Structured Matrix Factorization	Emanuele Bugliarello, Swayambhoo Jain, Vineeth Rakesh	In this paper, we address a key challenge faced by our company: predicting the efficiency of artists in rendering visual effects (VFX) in film shots.
283	Active Learning within Constrained Environments through Imitation of an Expert Questioner	Kalesha Bullard, Yannick Schroecker, Sonia Chernova	This work uses imitation learning to enable an agent in a constrained environment to concurrently reason about both its internal learning goals and environmental constraints externally imposed, all within its objective function.
284	Multi-View Active Learning for Video Recommendation	Jia-Jia Cai, Jun Tang, Qing-Guo Chen, Yao Hu, Xiaobo Wang, Sheng-Jun Huang	To train an effective recommender system with lower annotation cost, we propose an active learning approach to fully exploit the visual view of videos, while querying as few annotations as possible from the text view.
285	Learning Disentangled Semantic Representation for Domain Adaptation	Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao	Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data.
286	Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering	Bertrand Charpentier, Thomas Bonald	We introduce the tree sampling divergence (TSD), an information-theoretic metric for assessing the quality of the hierarchical clustering of a graph.
287	FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data	Haipeng Chen, Sushil Jajodia, Jing Liu, Noseong Park, Vadim Sokolov, V. S. Subrahmanian	In this paper, our goal is to find a way to augment the sub-table by generating a synthetic table from the released sub-table, under the constraints that the generated synthetic table (i) has similar statistics as the entire table, and (ii) preserves the functional dependencies of the released sub-table.
288	Theoretical Investigation of Generalization Bound for Residual Networks	Hao Chen, Zhanfeng Mo, Zhouwang Yang, Xiao Wang	This paper presents a framework for norm-based capacity control with respect to an lp,q-norm in weight-normalized Residual Neural Networks (ResNets).
289	Learning Semantic Annotations for Tabular Data	Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks, Charles Sutton	Unlike traditional lexical matching-based methods, we propose a deep prediction model that can fully exploit a table’s contextual semantics, including table locality features learned by a Hybrid NeuralNetwork (HNN), and inter-column semantics features learned by a knowledge base (KB) lookup and query answering algorithm.
290	Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network	Liang Chen, Yang Liu, Xiangnan He, Lianli Gao, Zibin Zheng	We contribute a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: 1) We design a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle’s representation; 2) We jointly model user-bundle interactions and user-item interactions in a multi-task manner to alleviate the scarcity of user-bundle interactions.
291	Cooperative Pruning in Cross-Domain Deep Neural Network Compression	Shangyu Chen, Wenya Wang, Sinno Jialin Pan	In this paper, we propose a method to perform cross-domain pruning by cooperatively training in both domains: taking advantage of data and a pre-trained model from the source domain to assist pruning in the target domain.
292	Extensible Cross-Modal Hashing	Tian-yi Chen, Lan Zhang, Shi-cong Zhang, Zi-long Li, Bai-chuan Huang	In this work, we propose a novel extensible cross-modal hashing (ECMH) to enable highly efficient and low-cost model extension.
293	Semi-supervised User Profiling with Heterogeneous Graph Attention Networks	Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo, Dawei Yin, Yongdong Zhang	In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning.
294	ActiveHNE: Active Heterogeneous Network Embedding	Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang	In DHNE, we introduce a novel semi-supervised heterogeneous network embedding method based on graph convolutional neural network.
295	A Restart-based Rank-1 Evolution Strategy for Reinforcement Learning	Zefeng Chen, Yuren Zhou, Xiao-yu He, Siyu Jiang	To this end, this paper proposes a restart-based rank-1 evolution strategy for reinforcement learning.
296	Co-Attentive Multi-Task Learning for Explainable Recommendation	Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, Enhong Chen	In this paper, we propose a co-attentive multi-task learning model for explainable recommendation.
297	Variational Graph Embedding and Clustering with Laplacian Eigenmaps	Zitai Chen, Chuan Chen, Zong Zhang, Zibin Zheng, Qingsong Zou	To address this issue, we propose a deep probabilistic model, called Variational Graph Embedding and Clustering with Laplacian Eigenmaps (VGECLE), which learns node embeddings and assigns node clusters simultaneously.
298	Deep Active Learning for Anchor User Prediction	Anfeng Cheng, Chuan Zhou, Hong Yang, Jia Wu, Lei Li, Jianlong Tan, Li Guo	To this end, we present a deep active learning model for anchor user prediction (DALAUP for short).
299	Success Prediction on Crowdfunding with Multimodal Deep Learning	Chaoran Cheng, Fei Tan, Xiurui Hou, Zhi Wei	In this work, we designed and evaluated advanced neural network schemes that combine information from different modalities to study the influence of sophisticated interactions among textual, visual, and metadata on project success prediction.
300	Approximate Optimal Transport for Continuous Densities with Copulas	Jinjin Chi, Jihong Ouyang, Ximing Li, Yang Wang, Meng Wang	To this end, we develop a novel continuous OT method, namely Copula OT (Cop-OT).
301	Ornstein Auto-Encoders	Youngwon Choi, Joong-Ho Won	We propose the Ornstein auto-encoder (OAE), a representation learning model for correlated data.
302	A Strongly Asymptotically Optimal Agent in General Environments	Michael K. Cohen, Elliot Catt, Marcus Hutter	We present an algorithm for a policy whose value approaches the optimal value with probability 1 in all computable probabilistic environments, provided the agent has a bounded horizon.
303	Extrapolating Paths with Graph Neural Networks	Jean-Baptiste Cordonnier, Andreas Loukas	Our main contribution is a graph neural network called Gretel.
304	Recommending Links to Maximize the Influence in Social Networks	Federico Corò, Gianlorenzo D’Angelo, Yllka Velaj	In this paper we measure the popularity of a user by means of its social influence, which is its capability to influence other users’ opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created.
305	Three-Player Wasserstein GAN via Amortised Duality	Nhan Dam, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, Dinh Phung	We propose a new formulation for learning generative adversarial networks (GANs) using optimal transport cost (the general form of Wasserstein distance) as the objective criterion to measure the dissimilarity between target distribution and learned distribution.
306	Learn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples	Ariyam Das, Jin Wang, Sahil M. Gandhi, Jae Lee, Wei Wang, Carlo Zaniolo	In this paper, we efficiently employ statistical resampling techniques to build an online tree faster using fewer examples.
307	IRC-GAN: Introspective Recurrent Convolutional GAN for Text-to-video Generation	Kangle Deng, Tianyi Fei, Xin Huang, Yuxin Peng	To address these issues, we present a novel Introspective Recurrent Convolutional GAN (IRC-GAN) approach.
308	Marginal Posterior Sampling for Slate Bandits	Maria Dimakopoulou, Nikos Vlassis, Tony Jebara	We introduce a new Thompson sampling-based algorithm, called marginal posterior sampling, for online slate bandits, that is characterized by three key ideas.
309	Reinforced Negative Sampling for Recommendation with Exposure Data	Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, Depeng Jin	In this work, we improve the negative sampler by integrating the exposure data.
310	Group Reconstruction and Max-Pooling Residual Capsule Network	Xinpeng Ding, Nannan Wang, Xinbo Gao, Jie Li, Xiaoyu Wang	In order to solve these shortcomings, this paper proposes a group reconstruction and max-pooling residual capsule network (GRMR-CapsNet).
311	Crafting Efficient Neural Graph of Large Entropy	Minjing Dong, Hanting Chen, Yunhe Wang, Chang Xu	We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture.
312	Joint Link Prediction and Network Alignment via Cross-graph Embedding	Xingbo Du, Junchi Yan, Hongyuan Zha	In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation.
313	Fast Algorithm for K-Truss Discovery on Public-Private Graphs	Soroush Ebadian, Xin Huang	This paper aims at finding k-truss efficiently in public-private graphs.
314	Mindful Active Learning	Zhila Esna Ashari, Hassan Ghasemzadeh	We propose a novel active learning framework for activity recognition using wearable sensors.
315	iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow	Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu	To enhance the social coding security, in this paper, we propose to automate cross-platform user identification between GitHub and Stack Overflow to combat the attackers who attempt to poison the modern software programming ecosystem.
316	Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space	Zhou Fan, Rui Su, Weinan Zhang, Yong Yu	In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks.
317	GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction	Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, Chunhong Pan	To capture the global dynamic spatial-temporal correlations, we propose a Global Spatial-Temporal Network (GSTNet), which consists of several layers of spatial-temporal blocks.
318	Partial Label Learning by Semantic Difference Maximization	Lei Feng, Bo An	By exploiting such dissimilarity relationships from label space, we propose a novel approach that aims to maximize the latent semantic differences of the two instances whose ground-truth labels are definitely different, while training the desired model simultaneously, thereby continually enlarging the gap of label confidences between two instances of different classes.
319	Deep Session Interest Network for Click-Through Rate Prediction	Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, Keping Yang	Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users’ multiple historical sessions in their behavior sequences.
320	Curriculum Learning for Cumulative Return Maximization	Francesco Foglino, Christiano Coletto Christakou, Ricardo Luna Gutierrez, Matteo Leonetti	We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes.
321	Advocacy Learning: Learning through Competition and Class-Conditional Representations	Ian Fox, Jenna Wiens	We introduce advocacy learning, a novel supervised training scheme for attention-based classification problems.
322	Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing	Chaoyou Fu, Liangchen Song, Xiang Wu, Guoli Wang, Ran He	This paper proposes a simple yet effective Neurons Merging Layer (NMLayer) for deep supervised hashing.
323	Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces	Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, Changjie Fan	Our work fills this gap by proposing two novel algorithms: Deep Multi-Agent Parameterized Q-Networks (Deep MAPQN) and Deep Multi-Agent Hierarchical Hybrid Q-Networks (Deep MAHHQN).
324	Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards	Zhao-Yang Fu, De-Chuan Zhan, Xin-Chun Li, Yi-Xing Lu	In this paper, we focus on the investigation of reinforcement learning with more than one auxiliary reward.
325	RecoNet: An Interpretable Neural Architecture for Recommender Systems	Francesco Fusco, Michalis Vlachos, Vasileios Vasileiadis, Kathrin Wardatzky, Johannes Schneider	We present a simple neural architecture for recommender systems that lifts several of these shortcomings.
326	Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation	Yang Gao, Christian M. Meyer, Mohsen Mesgar, Iryna Gurevych	We propose RELIS, a novel RL paradigm that learns a reward function with Learning-to-Rank (L2R) algorithms at training time and uses this reward function to train an input-specific RL policy at test time.
327	Fully Distributed Bayesian Optimization with Stochastic Policies	Javier Garcia-Barcos, Ruben Martinez-Cantin	In this paper, we present a new method for fully distributed Bayesian optimization, which can be combined with any acquisition function.
328	Scalable Semi-Supervised SVM via Triply Stochastic Gradients	Xiang Geng, Bin Gu, Xiang Li, Wanli Shi, Guansheng Zheng, Heng Huang	To address this problem, in this paper, we propose a triply stochastic gradient algorithm for S3VM, called TSGS3VM.
329	Perception-Aware Point-Based Value Iteration for Partially Observable Markov Decision Processes	Mahsa Ghasemi, Ufuk Topcu	We develop a novel point-based value iteration algorithm that incorporates this greedy strategy to pick perception actions for each sampled belief point in each iteration.
330	Efficient Regularization Parameter Selection for Latent Variable Graphical Models via Bi-Level Optimization	Joachim Giesen, Frank Nussbaum, Christopher Schneider	Here, we develop an adaptive variant of Benson’s algorithm for the semidefinite case and show that it keeps the known approximation and run time guarantees.
331	Using Natural Language for Reward Shaping in Reinforcement Learning	Prasoon Goyal, Scott Niekum, Raymond J. Mooney	In this work, we use natural language instructions to perform reward shaping.
332	Sketched Iterative Algorithms for Structured Generalized Linear Models	Qilong Gu, Arindam Banerjee	In this paper, we study sketched iterative algorithms, in particular sketched-PGD (projected gradient descent) and sketched-SVRG (stochastic variance reduced gradient) for structured generalized linear model, and illustrate that these methods continue to have geometric convergence to the statistical error under suitable assumptions.
333	SPINE: Structural Identity Preserved Inductive Network Embedding	Junliang Guo, Linli Xu, Jingchang Liu	In this paper we present SPINE, a method that can jointly capture the local proximity and proximities at any distance, while being inductive to efficiently deal with unseen nodes or networks.
334	Discriminative Sample Generation for Deep Imbalanced Learning	Ting Guo, Xingquan Zhu, Yang Wang, Fang Chen	In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions.
335	Affine Equivariant Autoencoder	Xifeng Guo, En Zhu, Xinwang Liu, Jianping Yin	To fill this gap, in this paper, we propose an affine equivariant autoencoder to learn features that are equivariant to the affine transformation in an unsupervised manner.
336	AdaLinUCB: Opportunistic Learning for Contextual Bandits	Xueying Guo, Xiaoxiao Wang, Xin Liu	In this paper, we propose and study opportunistic contextual bandits – a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations.
337	Zero-shot Learning with Many Classes by High-rank Deep Embedding Networks	Yuchen Guo, Guiguang Ding, Jungong Han, Hang Shao, Xin Lou, Qionghai Dai	To address this issue, we propose a novel approach, termed as High-rank Deep Embedding Networks (GREEN), for ZSL with many classes.
338	Landmark Selection for Zero-shot Learning	Yuchen Guo, Guiguang Ding, Jungong Han, Chenggang Yan, Jiyong Zhang, Qionghai Dai	Motivated by this idea, we propose a novel approach, termed as Landmark Selection(LAST) for ZSL.
339	MineRL: A Large-Scale Dataset of Minecraft Demonstrations	William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov	Therefore, we introduce a comprehensive, large-scale, simulator-paired dataset of human demonstrations: MineRL.
340	Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes	Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi	In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs).
341	Attribute Aware Pooling for Pedestrian Attribute Recognition	Kai Han, Yunhe Wang, Han Shu, Chuanjian Liu, Chunjing Xu, Chang Xu	We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes.
342	Network Embedding under Partial Monitoring for Evolving Networks	Yu Han, Jie Tang, Qian Chen	So in this paper, we study a novel and challenging problem, i.e., network embedding under partial monitoring for evolving networks.
343	Deep Active Learning with Adaptive Acquisition	Manuel Haussmann, Fred Hamprecht, Melih Kandemir	We present a method to break this vicious circle by defining the acquisition function as a learning predictor and training it by reinforcement feedback collected from each labeling round.
344	One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks	Tao He, Yuan-Fang Li, Lianli Gao, Dongxiang Zhang, Jingkuan Song	In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images.
345	Deliberation Learning for Image-to-Image Translation	Tianyu He, Yingce Xia, Jianxin Lin, Xu Tan, Di He, Tao Qin, Zhibo Chen	In this paper, we go beyond this learning framework by considering an additional polishing step on the output image.
346	Online Learning from Capricious Data Streams: A Generative Approach	Yi He, Baijun Wu, Di Wu, Ege Beyazit, Sheng Chen, Xindong Wu	We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve performance with theoretical analysis.
347	Learning Topic Models by Neighborhood Aggregation	Ryohei Hisano	The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words.
348	Group-based Learning of Disentangled Representations with Generalizability for Novel Contents	Haruo Hosoya	In this study, we introduce a novel deep generative model, called group-based variational autoencoders.
349	Robust Learning from Noisy Side-information by Semidefinite Programming	En-Liang Hu, Quanming Yao	Motivated by such a trend and needs, we pursue robustness in semi-definite programming (SDP) in this paper.
350	Hybrid Item-Item Recommendation via Semi-Parametric Embedding	Peng Hu, Rong Du, Yao Hu, Nan Li	In this paper, we propose a semi-parametric embedding framework for this problem.
351	Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Bandit	Yi-Qi Hu, Yang Yu, Jun-Da Liao	In this paper, we propose a cascaded approach for algorithm selection and hyper-parameter optimization.
352	Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm	Mengdi Huai, Hongfei Xue, Chenglin Miao, Liuyi Yao, Lu Su, Changyou Chen, Aidong Zhang	In this paper, we try to fill up this research gap and derive the generalization error bound for DML.
353	Privacy-aware Synthesizing for Crowdsourced Data	Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang	To address this challenge, in this paper, we propose a novel privacy-aware synthesizing method (i.e., PrisCrowd) for crowdsourced data, based on which the data collector can release users’ data with strong privacy protection for their private information, while at the same time, the data analyzer can achieve good utility from the released data.
354	Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization	Feihu Huang, Shangqian Gao, Songcan Chen, Heng Huang	In the paper, thus, we propose a class of fast zeroth-order stochastic ADMM methods (\emph{i.e.}, ZO-SVRG-ADMM and ZO-SAGA-ADMM) for solving nonconvex problems with multiple nonsmooth penalties, based on the coordinate smoothing gradient estimator.
355	Nostalgic Adam: Weighting More of the Past Gradients When Designing the Adaptive Learning Rate	Haiwen Huang, Chang Wang, Bin Dong	We therefore propose an algorithm called the Nostalgic Adam (NosAdam) with theoretically guaranteed convergence at the best known convergence rate.
356	Multi-view Spectral Clustering Network	Zhenyu Huang, Joey Tianyi Zhou, Xi Peng, Changqing Zhang, Hongyuan Zhu, Jiancheng Lv	In this paper, we propose a novel multi-view clustering method named multi-view spectral clustering network (MvSCN) which could be the first deep version of multi-view spectral clustering to the best of our knowledge.
357	Conditions on Features for Temporal Difference-Like Methods to Converge	Marcus Hutter, Samuel Yang-Zhao, Sultan Javed Majeed	In this paper, we provide a complete characterization of non-uniqueness issues for a large class of reinforcement learning algorithms, simultaneously unifying many counter-examples to convergence in a theoretical framework.
358	Entangled Kernels	Riikka Huusari, Hachem Kadri	We propose an efficient two-step algorithm for this framework, where the entangled kernel is learned based on a novel extension of kernel alignment to operator-valued kernels.
359	Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks	Tsuyoshi Idé, Rudy Raymond, Dzung T. Phan	To this end, we propose a new framework for collaborative dictionary learning.
360	SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets	Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Tushar Chandra, Craig Boutilier	Under mild assumptions on user choice behavior, we show that the long-term value (LTV) of a slate can be decomposed into a tractable function of its component item-wise LTVs.
361	Accelerating Extreme Classification via Adaptive Feature Agglomeration	Ankit Jalan, Purushottam Kar	We propose DEFRAG, an adaptive feature agglomeration technique to accelerate extreme classification algorithms.
362	Assumed Density Filtering Q-learning	Heejin Jeong, Clark Zhang, George J. Pappas, Daniel D. Lee	In this paper, we introduce a novel Bayesian approach to off-policy TD methods, called as ADFQ, which updates beliefs on state-action values, Q, through an online Bayesian inference method known as Assumed Density Filtering.
363	Learning to Learn Gradient Aggregation by Gradient Descent	Jinlong Ji, Xuhui Chen, Qianlong Wang, Lixing Yu, Pan Li	In this work, motivated by learning to learn, we propose a meta-learning approach to coordinate the learning process in the master-slave type of distributed systems.
364	Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks	Taoran Ji, Zhiqian Chen, Nathan Self, Kaiqun Fu, Chang-Tien Lu, Naren Ramakrishnan	In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences.
365	Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring	Xiaowei Jia, Mengdie Wang, Ankush Khandelwal, Anuj Karpatne, Vipin Kumar	In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution.
366	Dynamic Hypergraph Neural Networks	Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, Yue Gao	To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC).
367	Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty	Junyang Jiang, Deqing Yang, Yanghua Xiao, Chenlu Shen	Addressing this problem, we propose a unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences exhibited by some users, resulting in better user representations and recommendation performance.
368	Robust Low-Tubal-Rank Tensor Completion via Convex Optimization	Qiang Jiang, Michael Ng	This paper considers the problem of recovering multidimensional array, in particular third-order tensor, from a random subset of its arbitrarily corrupted entries.
369	CensNet: Convolution with Edge-Node Switching in Graph Neural Networks	Xiaodong Jiang, Pengsheng Ji, Sheng Li	In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features.
370	Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks	Di Jin, Bingyi Li, Pengfei Jiao, Dongxiao He, Weixiong Zhang	Specifically, as a part of the dual decoder, we develop a novel method based on a Gaussian mixture model and the block model to reconstruct network structures.
371	Hypergraph Induced Convolutional Manifold Networks	Taisong Jin, Liujuan Cao, Baochang Zhang, Xiaoshuai Sun, Cheng Deng, Rongrong Ji	Specifically, two innovative designs are provides: 1) our manifold preserving method is implemented based on a mini-batch, which can be efficiently plugged into the existing DCNN training pipelines and be scalable for large datasets; 2) a robust hypergraph is built for each mini-batch, which not only offers a strong robustness against typical noise, but also captures the variances from multiple features.
372	Submodular Batch Selection for Training Deep Neural Networks	K J Joseph, Vamshi Teja R, Krishnakant Singh, Vineeth N Balasubramanian	We design an efficient, greedy algorithm which can give high-quality solutions to this NP-hard combinatorial optimization problem.
373	Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning	Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius, Danny Lange	In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players.
374	Interactive Teaching Algorithms for Inverse Reinforcement Learning	Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher, Adish Singla	We present an interactive teaching framework where a teacher adaptively chooses the next demonstration based on learner’s current policy.
375	Multiple Partitions Aligned Clustering	Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, Zenglin Xu	Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partitions.
376	Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI	Eoin M. Kenny, Mark T. Keane	In this paper, twin-systems are described to address the eXplainable artificial intelligence (XAI) problem, where a black box model is mapped to a white box “twin” that is more interpretable, with both systems using the same dataset.
377	What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features	Pasha Khosravi, Yitao Liang, YooJung Choi, Guy Van den Broeck	In this paper, we propose a novel framework that classifies examples with missing features by computing the expected prediction with respect to a feature distribution.
378	Outlier Detection for Time Series with Recurrent Autoencoder Ensembles	Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen	We propose two solutions to outlier detection in time series based on recurrent autoencoder ensembles.
379	DeepMellow: Removing the Need for a Target Network in Deep Q-Learning	Seungchan Kim, Kavosh Asadi, Michael Littman, George Konidaris	We argue that using a target network is incompatible with online reinforcement learning, and it is possible to achieve faster and more stable learning without a target network when we use Mellowmax, an alternative softmax operator.
380	Sequential and Diverse Recommendation with Long Tail	Yejin Kim, Kwangseob Kim, Chanyoung Park, Hwanjo Yu	Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse items without compromising significant accuracy.To learn temporal preference on diverse items as well as on general items, we cluster and relocate consumed long tail items to make a pseudo ground truth for diverse items and learn the preference on long tail using recurrent neural network, which enables us to directly learn a ranking function.
381	Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks	Qiuqiang Kong, Yong Xu, Philip J. B. Jackson, Wenwu Wang, Mark D. Plumbley	We propose a synthesizing-decomposition (S-D) approach to solve the single-channel separation and deconvolution problem.
382	Autoregressive Policies for Continuous Control Deep Reinforcement Learning	Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra	We introduce a family of stationary autoregressive (AR) stochastic processes to facilitate exploration in continuous control domains.
383	Adaptive Ensemble Active Learning for Drifting Data Stream Mining	Bartosz Krawczyk, Alberto Cano	In this paper, we propose a novel active learning approach based on ensemble algorithms that is capable of using multiple base classifiers during the label query process.
384	Learning Sound Events from Webly Labeled Data	Anurag Kumar, Ankit Shah, Alexander Hauptmann, Bhiksha Raj	In this work, we introduce webly labeled learning for sound events which aims to remove human supervision altogether from the learning process.
385	Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models	Nupur Kumari, Mayank Singh, Abhishek Sinha, Harshitha Machiraju, Balaji Krishnamurthy, Vineeth N Balasubramanian	Leveraging this information, we introduce a new technique Latent Adversarial Training (LAT) which comprises of fine-tuning the adversarially trained models to ensure the robustness at the feature layers.
386	Perturbed-History Exploration in Stochastic Multi-Armed Bandits	Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier	We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit.
387	Meta Reinforcement Learning with Task Embedding and Shared Policy	Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang	In this paper, we propose to capture the shared information on the one hand and meta-learn how to quickly abstract the specific information about a task on the other hand.
388	The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations	Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki	This paper focuses on the case of counterfactual explanations and asks whether the generated instances can be justified, i.e. continuously connected to some ground-truth data.
389	Correlation-Sensitive Next-Basket Recommendation	Duc-Trong Le, Hady W. Lauw, Yuan Fang	Towards this objective, we develop a hierarchical network architecture codenamed Beacon to model basket sequences.
390	Learning Multiple Maps from Conditional Ordinal Triplets	Dung D. Le, Hady W. Lauw	We formulate this problem as conditional ordinal embedding, which learns a distinct low-dimensional representation conditioned on each aspect, yet allows collaboration across aspects via a shared representation.
391	Learning Generative Adversarial Networks from Multiple Data Sources	Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Phung	In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources.
392	Action Space Learning for Heterogeneous User Behavior Prediction	Dongha Lee, Chanyoung Park, Hyunjun Ju, Junyoung Hwang, Hwanjo Yu	This paper proposes a novel metric learning method, called METAS, to jointly model heterogeneous user behaviors.
393	Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks	Seungwon Lee, James Stokes, Eric Eaton	Inspired by this idea, we introduce a novel architecture for sharing latent factorized representations in convolutional neural networks (CNNs).
394	Similarity Preserving Representation Learning for Time Series Clustering	Qi Lei, Jinfeng Yi, Roman Vaculin, Lingfei Wu, Inderjit S. Dhillon	In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix.
395	Differentially Private Optimal Transport: Application to Domain Adaptation	Nam LeTien, Amaury Habrard, Marc Sebban	In this paper, we address the challenging task of privacy preserving domain adaptation by optimal transport.
396	Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model	Chang Li, Maarten de Rijke	In this paper, we study the online learning to rank problem in a non-stationary environment where user preferences change abruptly at an unknown moment in time.
397	A Review-Driven Neural Model for Sequential Recommendation	Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan	In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering user’s intrinsic preference (long-term) and sequential patterns (short-term).
398	Hierarchical Representation Learning for Bipartite Graphs	Chong Li, Kunyang Jia, Dan Shen, C.J. Richard Shi, Hongxia Yang	We propose to cluster the users into a set of communities and make recommendations based on the information of the users in the community collectively.
399	Multi-Class Learning using Unlabeled Samples: Theory and Algorithm	Jian Li, Yong Liu, Rong Yin, Weiping Wang	In this paper, we investigate the generalization performance of multi-class classification, for which we obtain a shaper error bound by using the notion of local Rademacher complexity and additional unlabeled samples, substantially improving the state-of-the-art bounds in existing multi-class learning methods.
400	Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis	Jian Li, Yong Liu, Rong Yin, Weiping Wang	In this paper, we propose an efficient graph-based semi-supervised algorithm with a sound theoretical guarantee.
401	Dense Transformer Networks for Brain Electron Microscopy Image Segmentation	Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, Shuiwang Ji	In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data.
402	Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting	Longyuan Li, Junchi Yan, Xiaokang Yang, Yaohui Jin	We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets.
403	Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss	Pengcheng Li, Jinfeng Yi, Bowen Zhou, Lijun Zhang	In this paper, we improve the robustness of DNNs by utilizing techniques of Distance Metric Learning.
404	Flexible Multi-View Representation Learning for Subspace Clustering	Ruihuang Li, Changqing Zhang, Qinghua Hu, Pengfei Zhu, Zheng Wang	In this paper, we propose to conduct subspace clustering based on Flexible Multi-view Representation (FMR) learning, which avoids using partial information for data reconstruction.
405	Improved Algorithm on Online Clustering of Bandits	Shuai Li, Wei Chen, Shuai Li, Kwong-Sak Leung	A more efficient algorithm is proposed with simple set structures to represent clusters.
406	Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest	Xiaosheng Li, Jessica Lin, Liang Zhao	This paper presents a novel time series clustering algorithm that has linear time complexity.
407	Learning Network Embedding with Community Structural Information	Yu Li, Ying Wang, Tingting Zhang, Jiawei Zhang, Yi Chang	Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning.
408	ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network	Zhangheng Li, Jia-Xing Zhong, Jingjia Huang, Tao Zhang, Thomas Li, Ge Li	In this paper, we introduce a novel MANN, the Auto-addressing and Recurrent Memory Integrating Network (ARMIN) to address these issues.
409	Deep Adversarial Multi-view Clustering Network	Zhaoyang Li, Qianqian Wang, Zhiqiang Tao, Quanxue Gao, Zhaohua Yang	In this paper, we propose a novel multi-view clustering method, named Deep Adversarial Multi-view Clustering (DAMC) network, to learn the intrinsic structure embedded in multi-view data.
410	GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks	Ziyao Li, Liang Zhang, Guojie Song	We propose GCN-LASE (GCN with Link Attributes and Sampling Estimation), a novel GCN model taking both node and link attributes as inputs.
411	Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval	Xiaoyuan Liang, Martin Renqiang Min, Hongyu Guo, Guiling Wang	In this paper, we propose a regularized autoencoder framework to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of symbols or data points, aiming at capturing essential semantic structures of data.
412	Worst-Case Discriminative Feature Selection	Shuangli Liao, Quanxue Gao, Feiping Nie, Yang Liu, Xiangdong Zhang	In this paper, we propose a new criterion for discriminative feature selection, worst-case discriminative feature selection (WDFS).
413	Image-to-Image Translation with Multi-Path Consistency Regularization	Jianxin Lin, Yingce Xia, Yijun Wang, Tao Qin, Zhibo Chen	In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training.
414	Balanced Clustering: A Uniform Model and Fast Algorithm	Weibo Lin, Zhu He, Mingyu Xiao	In this paper, we present a balanced clustering model that is to minimize the sum of squared distances to cluster centers, with uniform regularization functions to control the balance degree of the clustering results.
415	Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness	Chihuang Liu, Joseph JaJa	We propose a model that employs feature prioritization by a nonlinear attention module and L2 feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training.
416	Learning Instance-wise Sparsity for Accelerating Deep Models	Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu, Chang Xu	In contrast, we study this problem from a different perspective by respecting the difference between data.
417	Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L21-Norm Distances	Kai Liu, Lodewijk Brand, Hua Wang, Feiping Nie	In our objective, the orthonormal constraint is enforced to avoid degenerate solutions.
418	Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph	Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang	In this paper, we show that weakly-labeled data can significantly improve the performance of meta-learning on few-shot classification.
419	Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies	Wen Liu, Weixin Luo, Zhengxin Li, Peilin Zhao, Shenghua Gao	Under the assumption that normal events can be well predicted, we propose a Margin Learning Embedded Prediction (MLEP) framework.
420	Supervised Short-Length Hashing	Xingbo Liu, Xiushan Nie, Quan Zhou, Xiaoming Xi, Lei Zhu, Yilong Yin	To address this issue, in this study, we propose a novel supervised short-length hashing (SSLH).
421	Graph and Autoencoder Based Feature Extraction for Zero-shot Learning	Yang Liu, Deyan Xie, Quanxue Gao, Jungong Han, Shujian Wang, Xinbo Gao	In order to solve these problems, we formulate a novel framework named Graph and Autoencoder Based Feature Extraction (GAFE) to seek a low-rank mapping to preserve the sub-manifold of samples.
422	Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor	Yuanyuan Liu, Fanhua Shang, Licheng Jiao	In this paper, we design a novel and simple momentum to accelerate the classical SAGA algorithm, and propose a direct accelerated incremental gradient descent algorithm.
423	Omnidirectional Scene Text Detection with Sequential-free Box Discretization	Yuliang Liu, Sheng Zhang, Lianwen Jin, Lele Xie, Yaqiang Wu, Zhepeng Wang	To address this issue, in this paper, we propose a novel method called Sequential-free Box Discretization (SBD) by discretizing the bounding box into key edges (KE) which can further derive more effective methods to improve detection performance.
424	Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network	Zheng Liu, Yu Xing, Fangzhao Wu, Mingxiao An, Xing Xie	In this work, a novel user representation framework, Hi-Fi Ark, is proposed.
425	Learning Low-precision Neural Networks without Straight-Through Estimator (STE)	Zhi-Gang Liu, Matthew Mattina	We propose an alternative methodology called alpha-blending (AB), which quantizes neural networks to low precision using stochastic gradient descent (SGD).
426	Parametric Manifold Learning of Gaussian Mixture Models	Ziquan Liu, Lei Yu, Janet H. Hsiao, Antoni B. Chan	In this paper, we propose Parametric Manifold Learning of GMMs (PML-GMM), which learns a parametric mapping from a low-dimensional latent space to a high-dimensional GMM manifold.
427	Multi-Objective Generalized Linear Bandits	Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang	In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives.
428	Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning	Sihui Luo, Xinchao Wang, Gongfan Fang, Yao Hu, Dapeng Tao, Mingli Song	In this paper, we study a deep-model reusing task, where we are given as input pre-trained networks of heterogeneous architectures specializing in distinct tasks, as teacher models.
429	E²GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation	Yonghong Luo, Ying Zhang, Xiangrui Cai, Xiaojie Yuan	This paper proposes an end-to-end generative model E²GAN to impute missing values in multivariate time series.
430	Weakly Supervised Multi-Label Learning via Label Enhancement	JiaQi Lv, Ning Xu, RenYi Zheng, Xin Geng	In this paper, a novel two-stage strategy named Weakly Supervised Multi-label Learning via Label Enhancement (WSMLLE) is proposed to learn from weakly supervised data via label enhancement.
431	AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition	HaoJie Ma, Wenzhong Li, Xiao Zhang, Songcheng Gao, Sanglu Lu	In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition.
432	Monte Carlo Tree Search for Policy Optimization	Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang, Mykel J. Kochenderfer	This paper presents a method for policy optimization based on Monte-Carlo tree search and gradient-free optimization.
433	Coarse-to-Fine Image Inpainting via Region-wise Convolutions and Non-Local Correlation	Yuqing Ma, Xianglong Liu, Shihao Bai, Lei Wang, Dailan He, Aishan Liu	To address these problems, we first propose region-wise convolutions to locally deal with the different types of regions, which can help exactly reconstruct existing regions and roughly infer the missing ones from existing regions at the same time.
434	On Principled Entropy Exploration in Policy Optimization	Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Müller	In this paper, we investigate Exploratory Conservative Policy Optimization (ECPO), a policy optimization strategy that improves exploration behavior while assuring monotonic progress in a principled objective.
435	Anytime Bottom-Up Rule Learning for Knowledge Graph Completion	Christian Meilicke, Melisachew Wudage Chekol, Daniel Ruffinelli, Heiner Stuckenschmidt	We propose an anytime bottom-up technique for learning logical rules from large knowledge graphs.
436	Unsupervised Hierarchical Temporal Abstraction by Simultaneously Learning Expectations and Representations	Katherine Metcalf, David Leake	This paper presents ENHAnCE, an algorithm that simultaneously learns a predictive model of the input stream and generates representations of the concepts being observed.
437	Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems	Fei Mi, Minlie Huang, Jiyong Zhang, Boi Faltings	In this paper, we study NLG in a low-resource setting to generate sentences in new scenarios with handful training examples.
438	Robust Flexible Feature Selection via Exclusive L21 Regularization	Di Ming, Chris Ding	Thus, in this paper, we introduce a novel regularization called “exclusive L21”, which is short for “L21 with exclusive lasso”, towards robust flexible feature selection.
439	Advantage Amplification in Slowly Evolving Latent-State Environments	Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier	In this work, we identify and analyze several key hurdles for RL in such environments, including belief state error and small action advantage.
440	Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control	Jeremy Morton, Freddie D. Witherden, Mykel J. Kochenderfer	We introduce the Deep Variational Koopman (DVK) model, a method for inferring distributions over observations that can be propagated linearly in time.
441	DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems	Nikhil Muralidhar, Sathappan Muthiah, Naren Ramakrishnan	In this paper, we propose DyAt (Dynamic Attention) networks, a novel deep learning sequence to sequence (Seq2Seq) model with a novel hierarchical attention mechanism for long-term time series state forecasting.
442	Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization	Mehrnaz Najafi, Lifang He, Philip S. Yu	In this paper, we propose a novel method for Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization (OR-MSTC), which is a technique capable of dealing with missing values and outliers in multi-aspect streaming tensor data.
443	Incremental Learning of Planning Actions in Model-Based Reinforcement Learning	Jun Hao Alvin Ng, Ronald P. A. Petrick	We introduce the novel concept of reliability as an intrinsic motivation for MBRL, and a method to learn from failure to prevent repeated instances of similar failures.
444	Group LASSO with Asymmetric Structure Estimation for Multi-Task Learning	Saullo H. G. Oliveira, André R. Gonçalves, Fernando J. Von Zuben	Our proposal is the first attempt in the literature to conceive a Group LASSO with asymmetric transference formulation, looking for the best of both worlds in a framework that admits the overlap of groups.
445	Hill Climbing on Value Estimates for Search-control in Dyna	Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White	In this work, we propose to generate such states by using the trajectory obtained from Hill Climbing (HC) the current estimate of the value function.
446	Indirect Trust is Simple to Establish	Elham Parhizkar, Mohammad Hossein Nikravan, Sandra Zilles	We propose a new and easy to implement method for computing indirect trust, based on a simple prediction with expert advice strategy as is often used in online learning.
447	Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks	Hogun Park, Jennifer Neville	In this paper, we propose a neural network architecture that jointly captures both temporal and static interaction patterns, which we call Temporal-Static-Graph-Net (TSGNet).
448	Improving Cross-lingual Entity Alignment via Optimal Transport	Shichao Pei, Lu Yu, Xiangliang Zhang	We propose a novel entity alignment framework (OTEA), which dually optimizes the entity-level loss and group-level loss via optimal transport theory.
449	Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks	Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxin Ning, Kunfeng Lai, Philip S. Yu	In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model.
450	A Practical Semi-Parametric Contextual Bandit	Yi Peng, Miao Xie, Jiahao Liu, Xuying Meng, Nan Li, Cheng Yang, Tao Yao, Rong Jin	In this paper, we formulate a novel Semi-Parametric Contextual Bandit Problem to relax this assumption.
451	Graph Space Embedding	João Pereira, Albert K. Groen, Erik S. G. Stroes, Evgeni Levin	We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required.
452	An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents	Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman	This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library.
453	Improving representation learning in autoencoders via multidimensional interpolation and dual regularizations	Sheng Qian, Guanyue Li, Wen-Ming Cao, Cheng Liu, Si Wu, Hau San Wong	Specifically, we propose the multidimensional interpolation to increase the capability of data interpolation by randomly setting interpolation coefficients for each dimension of latent representations.
454	Scalable Bayesian Non-linear Matrix Completion	Xiangju Qin, Paul Blomstedt, Samuel Kaski	To solve the challenges regarding scalability and computation, we propose a data-parallel distributed computational approach with a restricted communication scheme.
455	Noise-Resilient Similarity Preserving Network Embedding for Social Networks	Zhenyu Qiu, Wenbin Hu, Jia Wu, ZhongZheng Tang, Xiaohua Jia	In this paper, we aim to exploit node similarity to address the problem of social network embedding with noise and propose a node similarity preserving (NSP) embedding method.
456	Fairwalk: Towards Fair Graph Embedding	Tahleen Rahman, Bartlomiej Surma, Michael Backes, Yang Zhang	We, therefore, propose a fairness-aware embedding method, namely Fairwalk, which extends node2vec.
457	Automated Machine Learning with Monte-Carlo Tree Search	Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag	A Monte-Carlo Tree Search Algorithm Selection and Configuration (Mosaic) approach is presented to tackle this mixed (combinatorial and continuous) expensive optimization problem on the structured search space of ML pipelines.
458	Successor Options: An Option Discovery Framework for Reinforcement Learning	Rahul Ramesh, Manan Tomar, Balaraman Ravindran	In this work, we instead adopt a complementary approach, where we attempt to discover options that navigate to landmark states.
459	Unifying the Stochastic and the Adversarial Bandits with Knapsack	Anshuka Rangi, Massimo Franceschetti, Long Tran-Thanh	We propose a novel algorithm EXP3.BwK and show that the expected regret of the algorithm is order optimal in the budget.
460	Label distribution learning with label-specific features	Tingting Ren, Xiuyi Jia, Weiwei Li, Lei Chen, Zechao Li	In this paper, we propose a novel LDL algorithm by leveraging label-specific features.
461	Label Distribution Learning with Label Correlations via Low-Rank Approximation	Tingting Ren, Xiuyi Jia, Weiwei Li, Shu Zhao	In this paper, we utilize both the global and local relevance among labels to provide more information for training model and propose a novel label distribution learning algorithm.
462	Closed-Loop Memory GAN for Continual Learning	Amanda Rios, Laurent Itti	Here we propose a cumulative closed-loop memory replay GAN (CloGAN) provided with external regularization by a small memory unit selected for maximum sample diversity.
463	Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay	Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly	Inspired from complementary learning systems theory, we address this challenge by learning a generative model that couples the current task to the past learned tasks through a discriminative embedding space.
464	Discovering Regularities from Traditional Chinese Medicine Prescriptions via Bipartite Embedding Model	Chunyang Ruan, Jiangang Ma, Ye Wang, Yanchun Zhang, Yun Yang	In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions.
465	A Degeneracy Framework for Scalable Graph Autoencoders	Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis	In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE).
466	Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents	Ritesh Sarkhel, Arnab Nandi	There are two major contributions of this work. First, we propose a spatial pyramid model to extract highly discriminative multi-scale feature descriptors from a visually rich document by leveraging the inherent hierarchy of its layout. Second, we propose a deterministic routing scheme for accelerating end-to-end inference by utilizing the spatial pyramid model.
467	SynthNet: Learning to Synthesize Music End-to-End	Florin Schimbinschi, Christian Walder, Sarah M. Erfani, James Bailey	We propose a specific architecture based on WaveNet, a convolutional autoregressive generative model designed for text to speech.
468	Weakly Supervised Multi-task Learning for Semantic Parsing	Bo Shao, Yeyun Gong, Junwei Bao, Jianshu Ji, Guihong Cao, Xiaola Lin, Nan Duan	We propose an effective method which substantially utilizes labeling information from other tasks to enhance the training of a semantic parser.
469	Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion	Junming Shao, Zhong Zhang, Zhongjing Yu, Jun Wang, Yi Zhao, Qinli Yang	In this paper, we propose a Cluster-driven Low-rank Matrix Completion (CLMC), for performing community detection and link prediction simultaneously in a unified framework.
470	On the Effectiveness of Low Frequency Perturbations	Yash Sharma, Gavin Weiguang Ding, Marcus A. Brubaker	This questions the use of the L-inf-norm, in particular, as a distortion metric, and, in turn, suggests that explicitly considering the frequency space is promising for learning robust models which better align with human perception.
471	A Part Power Set Model for Scale-Free Person Retrieval	Yunhang Shen, Rongrong Ji, Xiaopeng Hong, Feng Zheng, Xiaowei Guo, Yongjian Wu, Feiyue Huang	In this paper, to lighten the restriction of such fixed and coarse input alignment, an end-to-end part power set model with multi-scale features is proposed, which captures the discriminative parts of pedestrians from global to local, and from coarse to fine, enabling part-based scale-free person re-ID.
472	Rapid Performance Gain through Active Model Reuse	Feng Shi, Yu-Feng Li	In this paper, we propose the AcMR (Active Model Reuse) method for the rapid performance improvement problem.
473	A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification	Shaohuai Shi, Kaiyong Zhao, Qiang Wang, Zhenheng Tang, Xiaowen Chu	In this paper, we first provide theoretical proofs on the convergence of the gTop-k scheme for non-convex objective functions under certain analytic assumptions. We then derive the convergence rate of gTop-k S-SGD, which is at the same order as the vanilla mini-batch SGD.
474	Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization	Wanli Shi, Bin Gu, Xiang Li, Xiang Geng, Heng Huang	To address this problem, in this paper, we propose a novel scalable quadruply stochastic gradient algorithm (QSG-S2AUC) for nonlinear semi-supervised AUC optimization.
475	Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning	Wenjie Shi, Shiji Song, Cheng Wu	To ensure stable learning while eliminating the need of two separate critics for soft value functions, we leverage double sampling approach to making the soft Bellman equation tractable.
476	Gradient Boosting with Piece-Wise Linear Regression Trees	Yu Shi, Jian Li, Zhize Li	In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners.
477	The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning	Bonggun Shin, Hao Yang, Jinho D. Choi	This paper touches the core of neural models in NLP, word embeddings, and presents an embedding distillation framework that remarkably reduces the dimension of word embeddings without compromising accuracy.
478	A Principled Approach for Learning Task Similarity in Multitask Learning	Changjian Shui, Mahdieh Abbasi, Louis-Émile Robitaille, Boyu Wang, Christian Gagné	In this paper, we give a different perspective from a theoretical point of view to understand this practice.
479	Structure Learning for Safe Policy Improvement	Thiago D. Simão, Matthijs T. J. Spaan	To overcome these limitations we enhance a Factored SPI (FSPI) algorithm with different structure learning methods.
480	Play and Prune: Adaptive Filter Pruning for Deep Model Compression	Pravendra Singh, Vinay Kumar Verma, Piyush Rai, Vinay P. Namboodiri	We present a new min-max framework for filter-level pruning of CNNs.
481	Solving Continual Combinatorial Selection via Deep Reinforcement Learning	Hyungseok Song, Hyeryung Jang, Hai H. Tran, Se-eun Yoon, Kyunghwan Son, Donggyu Yun, Hyoju Chung, Yung Yi	In this paper, we present a deep RL algorithm to solve this issue by adopting the following key ideas.
482	Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning	Shihong Song, Jiayi Weng, Hang Su, Dong Yan, Haosheng Zou, Jun Zhu	Specifically, we present a hierarchical model that works in a manager-worker fashion over two levels of hierarchy.
483	Parallel Wasserstein Generative Adversarial Nets with Multiple Discriminators	Yuxin Su, Shenglin Zhao, Xixian Chen, Irwin King, Michael Lyu	In this paper, we solve the computation cost problem by speeding up the Wasserstein GANs from a well-designed communication efficient parallel architecture.
484	Finding Statistically Significant Interactions between Continuous Features	Mahito Sugiyama, Karsten Borgwardt	We propose an algorithm which overcomes the combinatorial explosion of the search space of higher-order interactions by deriving a lower bound on the p-value for each interaction, which enables us to massively prune interactions that can never reach significance and to thereby gain more statistical power.
485	Fast and Robust Multi-View Multi-Task Learning via Group Sparsity	Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka	To overcome these limitations, we propose a robust method with joint group-sparsity by decomposing feature parameters into a sum of two components,in which one saves relevant features (for Problem 1) and flexible view consistency (for Problem 2),while the other detects task-view outliers (for Problem 3).
486	Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning	Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka	In this paper, we propose a new method to directly handle these challenges based on multiplicative sparse feature decomposition.
487	Adversarial Imitation Learning from Incomplete Demonstrations	Mingfei Sun, Xiaojuan Ma	In this paper, we propose a novel algorithm called Action-Guided Adversarial Imitation Learning (AGAIL) that learns a pol- icy from demonstrations with incomplete action sequences, i.e., incomplete demonstrations.
488	Heavy-ball Algorithms Always Escape Saddle Points	Tao Sun, Dongsheng Li, Zhe Quan, Hao Jiang, Shengguo Li, Yong Dou	In this paper, we answer a question: can the nonconvex heavy-ball algorithms with random initialization avoid saddle points?
489	MEGAN: A Generative Adversarial Network for Multi-View Network Embedding	Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar	Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks.
490	Metric Learning on Healthcare Data with Incomplete Modalities	Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Jing Gao, Aidong Zhang	To tackle the aforementioned challenges, we propose a metric learning framework to perform missing modality completion and multi-modal metric learning simultaneously.
491	HMLasso: Lasso with High Missing Rate	Masaaki Takada, Hironori Fujisawa, Takeichiro Nishikawa	In this paper, we propose a novel Lasso-type regression method for high-dimensional data with high missing rates.
492	Deeply-learned Hybrid Representations for Facial Age Estimation	Zichang Tan, Yang Yang, Jun Wan, Guodong Guo, Stan Z. Li	In this paper, we propose a novel unified network named Deep Hybrid-Aligned Architecture for facial age estimation.
493	AugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation	Philip Tannor, Lior Rokach	In this paper we introduce a method for obtaining better results, by augmenting the features in the dataset between the iterations of GBDT.
494	Adversarial Graph Embedding for Ensemble Clustering	Zhiqiang Tao, Hongfu Liu, Jun Li, Zhaowen Wang, Yun Fu	In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process.
495	Hierarchical Inter-Attention Network for Document Classification with Multi-Task Learning	Bing Tian, Yong Zhang, Jin Wang, Chunxiao Xing	In this paper, we propose a multi-task learning framework to jointly train multiple related document classification tasks.
496	Image Captioning with Compositional Neural Module Networks	Junjiao Tian, Jean Oh	Inspired by the idea of the compositional neural module networks in the visual question answering task, we introduce a hierarchical framework for image captioning that explores both compositionality and sequentiality of natural language.
497	Imitation Learning from Video by Leveraging Proprioception	Faraz Torabi, Garrett Warnell, Peter Stone	Motivated by the fact that agents often also have access to their own internal states (i.e., proprioception), we propose and study an IfO algorithm that leverages this information in the policy learning process.
498	Exchangeability and Kernel Invariance in Trained MLPs	Russell Tsuchida, Fred Roosta, Marcus Gallagher	We show the sense in which the weights in MLPs are exchangeable.
499	Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning	Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov	In this work, we propose to approximate Bayesian neural networks (BNN) by Gaussian processes (GP), which allows us to update the uncertainty estimates of predictions efficiently without retraining the neural network while avoiding overconfident uncertainty prediction for out-of-sample points.
500	Object Detection based Deep Unsupervised Hashing	Rong-Cheng Tu, Xian-Ling Mao, Bo-Si Feng, Shu-ying Yu	Thus, in this paper, we propose a novel Object Detection based Deep Unsupervised Hashing method (ODDUH).
501	Ensemble-based Ultrahigh-dimensional Variable Screening	Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian	In this paper, we introduce a general ensemble-based framework to efficiently combine results from multiple variable screening methods.
502	Learning to Interpret Satellite Images using Wikipedia	Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon	To overcome this limitation, we construct a novel dataset called WikiSatNet by pairing geo-referenced Wikipedia articles with satellite imagery of their corresponding locations. We then propose two strategies to learn representations of satellite images by predicting properties of the corresponding articles from the images.
503	DeepCU: Integrating both Common and Unique Latent Information for Multimodal Sentiment Analysis	Sunny Verma, Chen Wang, Liming Zhu, Wei Liu	In this research, we first propose a novel deep architecture to extract the common information from the multi-mode representations. Furthermore, we propose unique networks to obtain the modality-specific information that enhances the generalization performance of our multimodal system. Finally, we integrate these two aspects of information via a fusion layer and propose a novel multimodal data fusion architecture, which we call DeepCU (Deep network with both Common and Unique latent information).
504	Interpolation Consistency Training for Semi-supervised Learning	Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz	We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm.
505	Sharing Experience in Multitask Reinforcement Learning	Tung-Long Vuong, Do-Van Nguyen, Tai-Long Nguyen, Cong-Minh Bui, Hai-Dang Kieu, Viet-Cuong Ta, Quoc-Long Tran, Thanh-Ha Le	In this paper, we propose a Sharing Experience Framework (SEF) for simultaneously training of multiple tasks.
506	Planning with Expectation Models	Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton	In this paper, we propose a sound way of using approximate expectation models for MBRL.
507	Recurrent Existence Determination Through Policy Optimization	Baoxiang Wang	Our algorithm employs a novel $k$-maximum aggregation layer and a new reward mechanism to address the issue of delayed rewards, which would have caused the instability of the training process.
508	Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach	Bin Wang, Guojun Qi, Sheng Tang, Tianzhu Zhang, Yunchao Wei, Linghui Li, Yongdong Zhang	To fully explore the limited pixel-level annotations from scribbles, we present a novel Boundary Perception Guidance (BPG) approach, which consists of two basic components, i.e., prediction refinement and boundary regression.
509	Attributed Graph Clustering: A Deep Attentional Embedding Approach	Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang	In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short).
510	Spectral Perturbation Meets Incomplete Multi-view Data	Hao Wang, Linlin Zong, Bing Liu, Yan Yang, Wei Zhou	In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering.
511	Measuring Structural Similarities in Finite MDPs	Hao Wang, Shaokang Dong, Ling Shao	In this paper, we investigate the structural similarities within a finite Markov decision process (MDP).
512	Discriminative and Correlative Partial Multi-Label Learning	Haobo Wang, Weiwei Liu, Yang Zhao, Chen Zhang, Tianlei Hu, Gang Chen	To fill this gap, a two-stage DiscRiminative and correlAtive partial Multi-label leArning (DRAMA) algorithm is presented in this work.
513	DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation	Huizhao Wang, Guanfeng Liu, An Liu, Zhixu Li, Kai Zheng	In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers, i.e., Item level Attention and Feature Level Self- attention, which are to pick out the most relevant items from the sequence of user’s historical behaviors, and extract the most relevant aspects of relevant items, respectively.
514	CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets	Jia Wang, Tong Sun, Benyuan Liu, Yu Cao, Hongwei Zhu	Inspired by stochastic recurrent models that successfully capture variability observed in natural sequential data such as speech and video, we propose CLVSA, a hybrid model that consists of stochastic recurrent networks, the sequence-to-sequence architecture, the self- and inter-attention mechanism, and convolutional LSTM units to capture variationally underlying features in raw financial trading data.
515	Classification with Label Distribution Learning	Jing Wang, Xin Geng	To solve the inconsistency, we propose in this paper a new Label Distribution Learning algorithm for Classification (LDL4C).
516	Attributed Subspace Clustering	Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi	Therefore, we propose an innovative model called attributed subspace clustering (ASC).
517	Deep Cascade Generation on Point Sets	Kaiqi Wang, Ke Chen, Kui Jia	For benefiting from its simple structure yet utilizing rich neighborhood information across points, this paper proposes a two-stage cascade model on point sets.
518	Discrete Binary Coding based Label Distribution Learning	Ke Wang, Xin Geng	In this paper, we propose a novel LDL method to address this issue, termed Discrete Binary Coding based Label Distribution Learning (DBC-LDL).
519	Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning	Lingxiao Wang, Quanquan Gu	We propose a generic differentially private iterative gradient hard threshoding algorithm with a linear convergence rate and strong utility guarantee.
520	MUSICAL: Multi-Scale Image Contextual Attention Learning for Inpainting	Ning Wang, Jingyuan Li, Lefei Zhang, Bo Du	To this end, in this study, we propose to use a multi-scale image contextual attention learning (MUSICAL) strategy that helps to flexibly handle richer background information while avoid to misuse of it.
521	Partial Label Learning with Unlabeled Data	Qian-Wei Wang, Yu-Feng Li, Zhi-Hua Zhou	In this paper, we propose the SSPL method to address this problem.
522	Heterogeneous Graph Matching Networks for Unknown Malware Detection	Shen Wang, Zhengzhang Chen, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, Philip S. Yu	To address the limitations of existing techniques, we propose MatchGNet, a heterogeneous Graph Matching Network model to learn the graph representation and similarity metric simultaneously based on the invariant graph modeling of the program’s execution behaviors.
523	Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks	Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Longbing Cao	Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session.
524	Multi-view Clustering via Late Fusion Alignment Maximization	Siwei Wang, Xinwang Liu, En Zhu, Chang Tang, Jiyuan Liu, Jingtao Hu, Jingyuan Xia, Jianping Yin	In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition.
525	COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning	Wenxiao Wang, Cong Fu, Jishun Guo, Deng Cai, Xiaofei He	To address the above problems, we develop a novel algorithm named as COP (correlation-based pruning), which can detect the redundant filters efficiently.
526	Position Focused Attention Network for Image-Text Matching	Yaxiong Wang, Hao Yang, Xueming Qian, Lin Ma, Jing Lu, Biao Li, Xin Fan	In this paper, we propose a novel position focused attention network (PFAN) to investigate the relation between the visual and the textual views.
527	Tag2Gauss: Learning Tag Representations via Gaussian Distribution in Tagged Networks	Yun Wang, Lun Du, Guojie Song, Xiaojun Ma, Lichen Jin, Wei Lin, Fei Sun	In this paper, we propose a tag representation learning model which takes tag-related node interaction into consideration, named Tag2Gauss.
528	Weak Supervision Enhanced Generative Network for Question Generation	Yutong Wang, Jiyuan Zheng, Qijiong Liu, Zhou Zhao, Jun Xiao, Yueting Zhuang	To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions.
529	Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation	Zekai Wang, Hongzhi Liu, Yingpeng Du, Zhonghai Wu, Xing Zhang	To address these problems, we propose a HIN based unified embedding model for recommendation, called HueRec.
530	Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations	Zhaodong Wang, Matthew E. Taylor	This paper introduces the Dynamic Reuse of Prior (DRoP) algorithm, which combines the offline knowledge (demonstrations recorded before learning) with online confidence-based performance analysis.
531	Hierarchical Diffusion Attention Network	Zhitao Wang, Wenjie Li	In this paper, we propose a hierarchical diffusion attention network (HiDAN), which adopts a non-sequential framework and two-level attention mechanisms, for diffusion prediction.
532	Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking	Nirandika Wanigasekara, Yuxuan Liang, Siong Thye Goh, Ye Liu, Joseph Jay Williams, David S. Rosenblum	To solve the MOCR-B problem, we present a novel algorithm, named Multi-Objective Utility-Upper Confidence Bound (MOU-UCB).
533	Learning for Tail Label Data: A Label-Specific Feature Approach	Tong Wei, Wei-Wei Tu, Yu-Feng Li	In this paper, we propose a novel method for the tail label learning problem.
534	Bayesian Uncertainty Matching for Unsupervised Domain Adaptation	Jun Wen, Nenggan Zheng, Junsong Yuan, Zhefeng Gong, Changyou Chen	To alleviate this issue, we propose an approximate joint distribution matching scheme by exploiting prediction uncertainty.
535	RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering	Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Jian Tan	To deal with these challenges, we propose a robust trend filtering algorithm based on robust statistics and sparse learning.
536	Neural News Recommendation with Attentive Multi-View Learning	Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie	In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information.
537	PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation	Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan	This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.
538	Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust	Qitian Wu, Lei Jiang, Xiaofeng Gao, Xiaochun Yang, Guihai Chen	In this paper, we propose TrustEV and take the view of multi-task learning to unite collaborative filtering for recommendation and network embedding for user trust.
539	Multi-View Multi-Label Learning with View-Specific Information Extraction	Xuan Wu, Qing-Guo Chen, Yao Hu, Dengbao Wang, Xiaodong Chang, Xiaobo Wang, Min-Ling Zhang	In this paper, a novel multi-view multi-label learning approach named SIMM is proposed which leverages shared subspace exploitation and view-specific information extraction.
540	Trend-Aware Tensor Factorization for Job Skill Demand Analysis	Xunxian Wu, Tong Xu, Hengshu Zhu, Le Zhang, Enhong Chen, Hui Xiong	To address these challenges, in this paper, we propose a trend-aware approach for fine-grained skill demand analysis.
541	Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference	Yongji Wu, Defu Lian, Shuowei Jin, Enhong Chen	We propose a novel model that utilizes Graph Convolutional Networks (GCNs) to learn user embeddings on the User Mobility Heterogeneous Graph in an unsupervised manner.
542	BPAM: Recommendation Based on BP Neural Network with Attention Mechanism	Wu-Dong Xi, Ling Huang, Chang-Dong Wang, Yin-Yu Zheng, Jianhuang Lai	To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM).
543	Incremental Few-Shot Learning for Pedestrian Attribute Recognition	Liuyu Xiang, Xiaoming Jin, Guiguang Ding, Jungong Han, Leida Li	In this work, we present a meta learning based method to address this issue.
544	Reparameterizable Subset Sampling via Continuous Relaxations	Sang Michael Xie, Stefano Ermon	We use this approach to sample subsets of features in an instance-wise feature selection task for model interpretability, subsets of neighbors to implement a deep stochastic k-nearest neighbors model, and sub-sequences of neighbors to implement parametric t-SNE by directly comparing the identities of local neighbors.
545	CFM: Convolutional Factorization Machines for Context-Aware Recommendation	Xin Xin, Bo Chen, Xiangnan He, Dong Wang, Yue Ding, Joemon Jose	In this paper, we propose Convolutional Factorization Machine (CFM) to address above limitations.
546	Adversarial Incomplete Multi-view Clustering	Cai Xu, Ziyu Guan, Wei Zhao, Hongchang Wu, Yunfei Niu, Beilei Ling	To eliminate all these drawbacks, in this work we present an Adversarial Incomplete Multi-view Clustering (AIMC) method.
547	Graph Contextualized Self-Attention Network for Session-based Recommendation	Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou	In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation.
548	Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs	Dongkuan Xu, Wei Cheng, Dongsheng Luo, Xiao Liu, Xiang Zhang	In this paper, we propose STAR, a spatio-temporal attentive recurrent network model, to deal with the above challenges.
549	Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators	Han Xu, Pengwei Liang, Wei Yu, Junjun Jiang, Jiayi Ma	In this paper, we propose a new end-to-end model, called dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions.
550	Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective	Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin	Moreover, leveraging our gradient-based attack, we propose the first optimization-based adversarial training for GNNs.
551	MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions	Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, Junzhou Zhao	To resolve these problems, we present {\em MR-GNN}, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (LSTMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs.
552	Commit Message Generation for Source Code Changes	Shengbin Xu, Yuan Yao, Feng Xu, Tianxiao Gu, Hanghang Tong, Jian Lu	In this paper, we propose CoDiSum to address the above two limitations.
553	Latent Semantics Encoding for Label Distribution Learning	Suping Xu, Lin Shang, Furao Shen	In this paper, we propose a novel algorithm, i.e., Latent Semantics Encoding for Label Distribution Learning (LSE-LDL), which learns the label distribution and implements feature selection simultaneously under the guidance of latent semantics.
554	Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation	Xiaofeng Xu, Ivor W. Tsang, Xiaofeng Cao, Ruiheng Zhang, Chuancai Liu	In this paper, we propose to learn image-specific attributes by graph-based attribute propagation.
555	Zero-shot Metric Learning	Xinyi Xu, Huanhuan Cao, Yanhua Yang, Erkun Yang, Cheng Deng	In this work, we tackle the zero-shot metric learning problem and propose a novel method abbreviated as ZSML, with the purpose to learn a distance metric that measures the similarity of unseen categories (even unseen datasets).
556	On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization	Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang	In this paper, we analyze gradient descent and stochastic gradient descent with extrapolation for finding an approximate first-order stationary point in smooth non-convex optimization problems.
557	Transfer of Temporal Logic Formulas in Reinforcement Learning	Zhe Xu, Ufuk Topcu	We study the transfer of knowledge between tasks in which the timing of the events matters.
558	Deep Spectral Kernel Learning	Hui Xue, Zheng-Fan Wu, Wei-Xiang Sun	In this paper, we propose a novel deep spectral kernel network (DSKN) to naturally integrate non-stationary and non-monotonic spectral kernels into elegant deep architectures in an interpretable way, which can be further generalized to cover most kernels.
559	Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification	Zhe Xue, Junping Du, Dawei Du, Wenqi Ren, Siwei Lyu	To address this problem, we propose a Deep Correlated Predictive Subspace Learning (DCPSL) method for incomplete multi-view semi-supervised classification.
560	Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks	Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, Zhiyuan Liu	In this paper, we propose a novel multi-scale diffusion prediction model based on reinforcement learning (RL).
561	Learning Strictly Orthogonal p-Order Nonnegative Laplacian Embedding via Smoothed Iterative Reweighted Method	Haoxuan Yang, Kai Liu, Hua Wang, Feiping Nie	In this work, we study LE that uses the p-th order of the L2-norm distances and satisfies both orthogonal and nonnegative constraints.
562	Low-Bit Quantization for Attributed Network Representation Learning	Hong Yang, Shirui Pan, Ling Chen, Chuan Zhou, Peng Zhang	To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy.
563	Topology Optimization based Graph Convolutional Network	Liang Yang, Zesheng Kang, Xiaochun Cao, Di Jin, Bo Yang, Yuanfang Guo	In this paper, we propose a novel Topology Optimization based Graph Convolutional Networks (TO-GCN) to fully utilize the potential information by jointly refining the network topology and learning the parameters of the FCN.
564	Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology	Liang Yang, Zhiyang Chen, Junhua Gu, Yuanfang Guo	To reduce the distortions induced by the topology while exploit more potentials of the attribute information, Dual Self-Paced Graph Convolutional Network (DSP-GCN) is proposed in this paper.
565	Masked Graph Convolutional Network	Liang Yang, Fan Wu, Yingkui Wang, Junhua Gu, Yuanfang Guo	In this paper, they are interpreted from the perspective of propagation, and accordingly categorized into symmetric and asymmetric propagation based methods.
566	Deep Multi-Task Learning with Adversarial-and-Cooperative Nets	Pei Yang, Qi Tan, Jieping Ye, Hanghang Tong, Jingrui He	In this paper, we propose a deep multi-Task learning model based on Adversarial-and-COoperative nets (TACO).
567	Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network	Wenmian Yang, Weijia Jia, Xiaojie Zhou, Yutao Luo	In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word Collocation Attention mechanism based on the topology structure among subtasks.
568	Comprehensive Semi-Supervised Multi-Modal Learning	Yang Yang, Ke-Tao Wang, De-Chuan Zhan, Hui Xiong, Yuan Jiang	To this end, in this paper, we propose a novel Comprehensive Multi-Modal Learning (CMML) framework, which can strike a balance between the consistency and divergency modalities by considering the insufficiency in one unified framework.
569	SPAGAN: Shortest Path Graph Attention Network	Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, Dacheng Tao	In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN).
570	On the Estimation of Treatment Effect with Text Covariates	Liuyi Yao, Sheng Li, Yaliang Li, Hongfei Xue, Jing Gao, Aidong Zhang	To address this challenge, we propose a conditional treatment-adversarial learning based matching method (CTAM).
571	Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction	Quanming Yao, Xiawei Guo, James Kwok, Weiwei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang	In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserving logistic regression by stacking.
572	Multi-View Multiple Clustering	Shixin Yao, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang	To this end, we propose a novel multi-view multiple clustering (MVMC) algorithm.
573	Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers	Jingwen Ye, Xinchao Wang, Yixin Ji, Kairi Ou, Mingli Song	In this paper, we treat such pre-trained networks as teachers and explore how to learn a target student network for customized tasks, using multiple teachers that handle different tasks.
574	A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment	Rui Ye, Xin Li, Yujie Fang, Hongyu Zang, Mingzhong Wang	In comparison with existing GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks.
575	Distributed Collaborative Feature Selection Based on Intermediate Representation	Xiucai Ye, Hongmin Li, Akira Imakura, Tetsuya Sakurai	In this paper, we propose a novel distributed method which allows collaborative feature selection for multiple parties without revealing their original data.
576	Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection	Yanfang Ye, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao	To model different types of entities (i.e., app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling.
577	Neural Network based Continuous Conditional Random Field for Fine-grained Crime Prediction	Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Bin Guo	To deal with it, in this paper, we propose a Neural Network based CCRF (NN-CCRF) model that formulates CCRF into an end-to-end neural network framework, which could reduce the complexity in model training and improve the overall performance.
578	BN-invariant Sharpness Regularizes the Training Model to Better Generalization	Mingyang Yi, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu	Our algorithm achieves considerably better performance than vanilla SGD over various experiment settings.
579	Geometric Understanding for Unsupervised Subspace Learning	Shihui Ying, Lipeng Cai, Changzhou He, Yaxin Peng	In this paper, we address the unsupervised subspace learning from a geometric viewpoint.
580	Belief Propagation Network for Hard Inductive Semi-Supervised Learning	Jaemin Yoo, Hyunsik Jeon, U Kang	In this work, we propose belief propagation networks (BPN), a novel approach to train a deep neural network in a hard inductive setting, where the test data are given without neighborhood information.
581	Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control	Kenny Young, Baoxiang Wang, Matthew E. Taylor	To address these issues, we propose Metatrace, a meta-gradient descent based algorithm to tune the step-size online.
582	Semi-supervised Three-dimensional Reconstruction Framework with GAN	Chong Yu	We propose a novel semi-supervised 3D reconstruction framework, namely SS-3D-GAN, which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge.
583	Interpreting and Evaluating Neural Network Robustness	Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen	This work aims to qualitatively interpret the adversarial attack and defense mechanisms through loss visualization, and establish a quantitative metric to evaluate the model’s intrinsic robustness.
584	VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders	Xianwen Yu, Xiaoning Zhang, Yang Cao, Min Xia	In this paper, a novel framework named VAEGAN is proposed to address the above issue.
585	Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation	Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie	In this paper, we improve the traditional RNN structure by proposing a time-aware controller and a content-aware controller, so that contextual information can be well considered to control the state transition.
586	Progressive Transfer Learning for Person Re-identification	Zhengxu Yu, Zhongming Jin, Long Wei, Jishun Guo, Jianqiang Huang, Deng Cai, Xiaofei He, Xian-Sheng Hua	In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the global information of the dataset when using mini-batch training.
587	DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns	Feng Yuan, Lina Yao, Boualem Benatallah	In this work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model (DARec) that is capable of extracting and transferring patterns from rating matrices only without relying on any auxillary information.
588	KCNN: Kernel-wise Quantization to Remarkably Decrease Multiplications in Convolutional Neural Network	Linghua Zeng, Zhangcheng Wang, Xinmei Tian	In this paper, we quantize the floating-point weights in each kernel separately to multiple bit planes to remarkably decrease multiplications.
589	Experience Replay Optimization	Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, Xia Hu	In this work, we consider learning a replay policy to optimize the cumulative reward.
590	Positive and Unlabeled Learning with Label Disambiguation	Chuang Zhang, Dexin Ren, Tongliang Liu, Jian Yang, Chen Gong	To solve this problem, this paper proposes a novel algorithm dubbed as “Positive and Unlabeled learning with Label Disambiguation” (PULD).
591	Generalized Majorization-Minimization for Non-Convex Optimization	Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng	In this paper, we propose a novel MM surrogate function from strictly upper bounding the objective to bounding the objective in expectation.
592	STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems	Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King	We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.
593	Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation	Jianpeng Zhang, Yutong Xie, Pingping Zhang, Hao Chen, Yong Xia, Chunhua Shen	In this paper, we propose the light-weight hybrid convolutional network (LW-HCN) to segment the liver and its tumors in CT volumes.
594	ProNE: Fast and Scalable Network Representation Learning	Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, Ming Ding	In this work, we present ProNE—a fast, scalable, and effective model, whose single-thread version is 10–400x faster than efficient network embedding benchmarks with 20 threads, including LINE, DeepWalk, node2vec, GraRep, and HOPE.
595	Towards Robust ResNet: A Small Step but a Giant Leap	Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan Kankanhalli	This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by a dynamical systems perspective.
596	High Dimensional Bayesian Optimization via Supervised Dimension Reduction	Miao Zhang, Huiqi Li, Steven Su	This paper directly introduces a supervised dimension reduction method, Sliced Inverse Regression (SIR), to high dimensional Bayesian optimization, which could effectively learn the intrinsic sub-structure of objective function during the optimization.
597	Efficient Non-parametric Bayesian Hawkes Processes	Rui Zhang, Christian Walder, Marian-Andrei Rizoiu, Lexing Xie	In this paper, we develop an efficient non-parametric Bayesian estimation of the kernel function of Hawkes processes.
598	Inferring Substitutable Products with Deep Network Embedding	Shijie Zhang, Hongzhi Yin, Qinyong Wang, Tong Chen, Hongxu Chen, Quoc Viet Hung Nguyen	In this paper, we propose a semisupervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationships between products by preserving the second-order proximity, negative first-order proximity and semantic similarity in a product co-purchasing graph based on user’s purchasing behaviours.
599	Quaternion Collaborative Filtering for Recommendation	Shuai Zhang, Lina Yao, Lucas Vinh Tran, Aston Zhang, Yi Tay	This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation.
600	Feature-level Deeper Self-Attention Network for Sequential Recommendation	Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, Xiaofang Zhou	In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation.
601	Attributed Graph Clustering via Adaptive Graph Convolution	Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu	In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs.
602	InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction	Xiaowang Zhang, Qiang Gao, Zhiyong Feng	In this paper, we present a neural network (InteractionNN) for sparse predictive analysis where hidden features of sparse data can be learned by multilevel feature interaction.
603	Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for Next-Day Air Quality Prediction	Yawen Zhang, Qin Lv, Duanfeng Gao, Si Shen, Robert Dick, Michael Hannigan, Qi Liu	This paper tackles this problem through three key contributions: (1) we leverage multi-source data, especially high-frequency grid-based weather data, to model air pollutant dynamics at station-level; (2) we add convolution operators on grid weather data to capture the impacts of various weather parameters on air pollutant variations; and (3) we automatically group (cross-domain) features based on their correlations, and propose multi-group Encoder-Decoder networks (MGED-Net) to effectively fuse multiple feature groups for next-day air quality prediction.
604	Taming the Noisy Gradient: Train Deep Neural Networks with Small Batch Sizes	Yikai Zhang, Hui Qu, Chao Chen, Dimitris Metaxas	In this paper, we tackle this problem by proposing a new framework for training deep neural network with small batches/noisy gradient.
605	Accelerated Inference Framework of Sparse Neural Network Based on Nested Bitmask Structure	Yipeng Zhang, Bo Du, Lefei Zhang, Rongchun Li, Yong Dou	In this paper, we propose a novel encoding approach on a sparse neural network after pruning.
606	DANE: Domain Adaptive Network Embedding	Yizhou Zhang, Guojie Song, Lun Du, Shuwen Yang, Yilun Jin	In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings.
607	ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling	Yuan Zhang, Xi Yang, Julie Ivy, Min Chi	To tackle these limitations, we propose an attention-based time-aware LSTM Networks (ATTAIN), to improve the interpretability of LSTM and to identify the critical previous events for current diagnosis by modeling the inherent time irregularity.
608	Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning	Zhao Zhang, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin	We propose a novel structured discriminative block- diagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification.
609	Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks	Zhu Zhang, Zhou Zhao, Zhijie Lin, Jingkuan Song, Xiaofei He	To tackle these problems, we propose a fast hierarchical convolutional self-attention encoder-decoder network.
610	Localizing Unseen Activities in Video via Image Query	Zhu Zhang, Zhou Zhao, Zhijie Lin, Jingkuan Song, Deng Cai	Thus, we consider a new task to localize unseen activities in videos via image queries, named Image-Based Activity Localization.
611	Multi-Prototype Networks for Unconstrained Set-based Face Recognition	Jian Zhao, Jianshu Li, Xiaoguang Tu, Fang Zhao, Yuan Xin, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng	In this paper, we address the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.
612	GAN-EM: GAN Based EM Learning Framework	Wentian Zhao, Shaojie Wang, Zhihuai Xie, Jing Shi, Chenliang Xu	To overcome such limitation, we propose a GAN based EM learning framework that can maximize the likelihood of images and estimate the latent variables.
613	Large Scale Evolving Graphs with Burst Detection	Yifeng Zhao, Xiangwei Wang, Hongxia Yang, Le Song, Jie Tang	Thus, in this paper, we design and implement a novel framework called BurstGraph which can capture both recurrent and consistent patterns, and especially unexpected bursty network changes.
614	AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN	Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, Jun Gao	In this paper, we propose AddGraph, a general end-to-end anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) with an attention model, which can capture both long-term patterns and the short-term patterns in dynamic graphs.
615	Metadata-driven Task Relation Discovery for Multi-task Learning	Zimu Zheng, Yuqi Wang, Quanyu Dai, Huadi Zheng, Dan Wang	In this paper, we, for the first time, introduce metadata into TRD for MTL and propose a novel Metadata Clustering method, which jointly uses historical samples and additional metadata to automatically exploit the true relatedness.
616	BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series	Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, Jing Ye	Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data.
617	Latent Distribution Preserving Deep Subspace Clustering	Lei Zhou, Xiao Bai, Dong Wang, Xianglong Liu, Jun Zhou, Edwin Hancock	In this paper, we propose a novel deep subspace clustering method based on a latent distribution-preserving autoencoder, which introduces a distribution consistency loss to guide the learning of distribution-preserving latent representation, and consequently enables strong capacity of characterizing the real-world data for subspace clustering.
618	Reinforcement Learning Experience Reuse with Policy Residual Representation	WenJi Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, Zhi-Hua Zhou	In this paper, we propose the policy residual representation (PRR) network, which can extract and store multiple levels of experience.
619	Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems	Xiao Zhou, Danyang Liu, Jianxun Lian, Xing Xie	Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN).
620	One-Shot Texture Retrieval with Global Context Metric	Kai Zhu, Wei Zhai, Zheng-Jun Zha, Yang Cao	In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary image.
621	HDI-Forest: Highest Density Interval Regression Forest	Lin Zhu, Jiaxing Lu, Yihong Chen	In this paper, we propose Highest Density Interval Regression Forest (HDI-Forest), a novel quality-based PI estimation method that is instead based on Random Forest.
622	Prediction of Mild Cognitive Impairment Conversion Using Auxiliary Information	Xiaofeng Zhu	In this paper, we propose a new feature selection method to exploit the issue of High Dimension Low Sample Size (HDLSS) for the prediction of Mild Cognitive Impairment (MCI) conversion.
623	Simultaneous Representation Learning and Clustering for Incomplete Multi-view Data	Wenzhang Zhuge, Chenping Hou, Xinwang Liu, Hong Tao, Dongyun Yi	To address these issues, in this paper, we propose a Simultaneous Representation Learning and Clustering (SRLC) method.
624	Persistence Bag-of-Words for Topological Data Analysis	Bartosz Zieli?ski, Micha? Lipi?ski, Mateusz Juda, Matthias Zeppelzauer, Pawe? D?otko	This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning.
625	Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains	Matthieu Zimmer, Paul Weng	In the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and later extended in Neural Fitted Actor Critic (NFAC).
626	Predicting the Visual Focus of Attention in Multi-Person Discussion Videos	Chongyang Bai, Srijan Kumar, Jure Leskovec, Miriam Metzger, Jay F. Nunamaker, V. S. Subrahmanian	Here we propose ICAF (Iterative Collective Attention Focus), a collective classification model to jointly learn the visual focus of attention of all people.
627	A Quantum-inspired Classical Algorithm for Separable Non-negative Matrix Factorization	Zhihuai Chen, Yinan Li, Xiaoming Sun, Pei Yuan, Jialin Zhang	In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem.
628	MLRDA: A Multi-Task Semi-Supervised Learning Framework for Drug-Drug Interaction Prediction	Xu Chu, Yang Lin, Yasha Wang, Leye Wang, Jiangtao Wang, Jingyue Gao	In this paper, we propose a multi-task semi-supervised learning framework MLRDA for DDI prediction.
629	Combining ADMM and the Augmented Lagrangian Method for Efficiently Handling Many Constraints	Joachim Giesen, Soeren Laue	Here, we address the problem of solving convex optimization problems with many convex constraints.
630	Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification	Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan	In order to increase the receptive field, we propose a novel deep Hierarchical Graph Convolutional Network (H-GCN) for semi-supervised node classification.
631	Playing Card-Based RTS Games with Deep Reinforcement Learning	Tianyu Liu, Zijie Zheng, Hongchang Li, Kaigui Bian, Lingyang Song	We present a deep model SEAT (selection-attention) to play card-based RTS games.
632	FSM: A Fast Similarity Measurement for Gene Regulatory Networks via Genes’ Influence Power	Zhongzhou Liu, Wenbin Hu	In this paper, a fast similarity measurement method called FSM for GRNs is proposed.
633	Pseudo Supervised Matrix Factorization in Discriminative Subspace	Jiaqi Ma, Yipeng Zhang, Lefei Zhang, Bo Du, Dapeng Tao	In this paper, a novel unsupervised matrix factorization method, Pseudo Supervised Matrix Factorization (PSMF), is proposed for data clustering.
634	Representation Learning-Assisted Click-Through Rate Prediction	Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu, Yanlong Du	In this paper, we propose DeepMCP, which models other types of relationships in order to learn more informative and statistically reliable feature representations, and in consequence to improve the performance of CTR prediction.
635	Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Networks	Wei Qiu, Haipeng Chen, Bo An	To this end, we propose a novel multi-agent reinforcement learning (RL) approach for DETC.
636	FireCast: Leveraging Deep Learning to Predict Wildfire Spread	David Radke, Anna Hessler, Dan Ellsworth	We present and evaluate a novel system, FireCast.
637	Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent	Shuheng Shen, Linli Xu, Jingchang Liu, Xianfeng Liang, Yifei Cheng	In this paper, we propose a computation and communication decoupled stochastic gradient descent (CoCoD-SGD) algorithm to run computation and communication in parallel to reduce the communication cost.
638	Randomized Adversarial Imitation Learning for Autonomous Driving	MyungJae Shin, Joongheon Kim	This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors.
639	Scaling Fine-grained Modularity Clustering for Massive Graphs	Hiroaki Shiokawa, Toshiyuki Amagasa, Hiroyuki Kitagawa	This paper proposes gScarf, which outputs fine-grained clusters within a short running time.
640	Node Embedding over Temporal Graphs	Uriel Singer, Ido Guy, Kira Radinsky	In this work, we present a method for node embedding in temporal graphs.
641	Medical Concept Embedding with Multiple Ontological Representations	Lihong Song, Chin Wang Cheong, Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon	In this paper, we propose a deep learning model called MMORE which alleviates this conflicting objective issue by allowing multiple representations to be inferred for each ontological category via an attention mechanism.
642	Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation	Yanan Xu, Yanmin Zhu, Yanyan Shen, Jiadi Yu	In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph).
643	Dual-Path in Dual-Path Network for Single Image Dehazing	Aiping Yang, Haixin Wang, Zhong Ji, Yanwei Pang, Ling Shao	To address this issue, the paper proposes a DPDP-Net (Dual-Path in Dual-Path network) framework by employing a hierarchical dual path network.
644	Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation	Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, Jingdong Wang	In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration.
645	Predicting dominance in multi-person videos	Chongyang Bai, Maksim Bolonkin, Srijan Kumar, Jure Leskovec, Judee Burgoon, Norah Dunbar, V. S. Subrahmanian	We introduce a novel family of variables called Dominance Rank.
646	Procedural Generation of Initial States of Sokoban	Dâmaris S. Bento, André G. Pereira, Levi H. S. Lelis	In this paper we deal with the task of generating hard and solvable initial states of Sokoban puzzles.
647	DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks	Huili Chen, Cheng Fu, Jishen Zhao, Farinaz Koushanfar	Our goal in this paper is to address the security concern on unknown DNN to NT attacks and ensure safe model deployment.
648	VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities	Xu Duan, Jingzheng Wu, Shouling Ji, Zhiqing Rui, Tianyue Luo, Mutian Yang, Yanjun Wu	In this paper, we define the accurate identification of vulnerabilities in similar code as a fine-grained vulnerability detection problem.
649	Real-Time Adversarial Attacks	Yuan Gong, Boyang Li, Christian Poellabauer, Yiyu Shi	In this paper, we propose a real-time adversarial attack scheme for machine learning models with streaming inputs.
650	Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach	Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, Qi Liu	To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS).
651	Model-Agnostic Adversarial Detection by Random Perturbations	Bo Huang, Yi Wang, Wei Wang	We propose a model-agnostic approach to resolve the problem by analysing the model responses to an input under random perturbations, and study the robustness of detecting norm-bounded adversarial distortions in a theoretical framework.
652	Musical Composition Style Transfer via Disentangled Timbre Representations	Yun-Ning Hung, I-Tung Chiang, Yi-An Chen, Yi-Hsuan Yang	This paper presents, to the best of our knowledge, the first deep learning models for rearranging music of arbitrary genres.
653	Multiple Policy Value Monte Carlo Tree Search	Li-Cheng Lan, Wei Li, Ting-Han Wei, I-Chen Wu	This paper introduces a new method called the multiple policy value MCTS (MPV-MCTS), which combines multiple policy value neural networks (PV-NNs) of various sizes to retain advantages of each network, where two PV-NNs f_S and f_L are used in this paper.
654	Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space	Linyi Li, Zexuan Zhong, Bo Li, Tao Xie	To address this issue, in this paper, we present our approach named Robustra for effectively improving the provable error bound of DNNs.
655	Dilated Convolution with Dilated GRU for Music Source Separation	Jen-Yu Liu, Yi-Hsuan Yang	Therefore, in this paper, we use stacked dilated convolutions as the backbone for music source separation.
656	Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks	Tianlong Liu, Yu Qi, Liang Shi, Jianan Yan	In this study, we propose a novel Locate-Then-Detect (LTD) system that can precisely detect Web threats in real-time by using attention-based deep neural networks.
657	Data Poisoning against Differentially-Private Learners: Attacks and Defenses	Yuzhe Ma, Xiaojin Zhu, Justin Hsu	We show that private learners are resistant to data poisoning attacks when the adversary is only able to poison a small number of items.
658	LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs	Weibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, Rong Zhou	In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence.
659	Decidability of Model Checking Multi-Agent Systems with Regular Expressions against Epistemic HS Specifications	Jakub Michaliszyn, Piotr Witkowski	In this paper we show that the model checking Multi-Agent Systems with regular expressions against the EHS specifications is decidable.
660	Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness	NhatHai Phan, Minh N. Vu, Yang Liu, Ruoming Jin, Dejing Dou, Xintao Wu, My T. Thai	In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples.
661	Demystifying the Combination of Dynamic Slicing and Spectrum-based Fault Localization	Sofia Reis, Rui Abreu, Marcelo d’Amorim	This paper reports on a comprehensive study to reassess the effects of combining DS with SFL.
662	Equally-Guided Discriminative Hashing for Cross-modal Retrieval	Yufeng Shi, Xinge You, Feng Zheng, Shuo Wang, Qinmu Peng	To handle this problem, we propose Equally-Guided Discriminative Hashing (EGDH), which jointly takes into consideration semantic structure and discriminability.
663	A Privacy Preserving Collusion Secure DCOP Algorithm	Tamir Tassa, Tal Grinshpoun, Avishai Yanay	In this study we propose the first privacy-preserving DCOP algorithm that is immune against coalitions, under the assumption of honest majority.
664	Two-Stage Generative Models of Simulating Training Data at The Voxel Level for Large-Scale Microscopy Bioimage Segmentation	Deli Wang, Ting Zhao, Nenggan Zheng, Zhefeng Gong	To provide a shortcut for this costly step, we propose a novel two-stage generative model for simulating voxel level training data based on a specially designed objective function of preserving foreground labels.
665	Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation	Di Wang, Jinhui Xu	In this paper, we study the sparse covariance matrix estimation problem in the local differential privacy model, and give a non-trivial lower bound on the non-interactive private minimax risk in the metric of squared spectral norm.
666	Principal Component Analysis in the Local Differential Privacy Model	Di Wang, Jinhui Xu	In this paper, we study the Principal Component Analysis (PCA) problem under the (distributed) non-interactive local differential privacy model.
667	Binarized Collaborative Filtering with Distilling Graph Convolutional Network	Haoyu Wang, Defu Lian, Yong Ge	Therefore, we propose a novel framework to convert the binary constrained optimization problem into an equivalent continuous optimization problem with a stochastic penalty.
668	Novel Collaborative Filtering Recommender Friendly to Privacy Protection	Jun Wang, Qiang Tang, Afonso Arriaga, Peter Y. A. Ryan	In this paper, we propose an efficient recommendation algorithm, named CryptoRec, which has two nice properties: (1) can estimate a new user’s preferences by directly using a model pre-learned from an expert dataset, and the new user’s data is not required to train the model; (2) can compute recommendations with only addition and multiplication operations.
669	Adversarial Examples for Graph Data: Deep Insights into Attack and Defense	Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu	In this paper, we propose techniques for both an adversarial attack and a defense against adversarial attacks.
670	FABA: An Algorithm for Fast Aggregation against Byzantine Attacks in Distributed Neural Networks	Qi Xia, Zeyi Tao, Zijiang Hao, Qun Li	In this paper, we propose FABA, a Fast Aggregation algorithm against Byzantine Attacks, which removes the outliers in the uploaded gradients and obtains gradients that are close to the true gradients.
671	BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference	Peichen Xie, Bingzhe Wu, Guangyu Sun	In this paper, we present a practical solution named BAYHENN for secure DNN inference.
672	Toward Efficient Navigation of Massive-Scale Geo-Textual Streams	Chengcheng Yang, Lisi Chen, Shuo Shang, Fan Zhu, Li Liu, Ling Shao	In this paper, we present NQ-tree, which combines new structure designs and self-tuning methods to navigate between update and search efficiency.
673	Temporal Pyramid Pooling Convolutional Neural Network for Cover Song Identification	Zhesong Yu, Xiaoshuo Xu, Xiaoou Chen, Deshun Yang	In this paper, Convolutional Neural Networks (CNNs) are used for representation learning toward this task.
674	Data Poisoning Attack against Knowledge Graph Embedding	Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, Kui Ren	To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph.
675	On Privacy Protection of Latent Dirichlet Allocation Model Training	Fangyuan Zhao, Xuebin Ren, Shusen Yang, Xinyu Yang	To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper.
676	K-Core Maximization: An Edge Addition Approach	Zhongxin Zhou, Fan Zhang, Xuemin Lin, Wenjie Zhang, Chen Chen	In this paper, we study the edge k-core problem: Given a graph G, an integer k and a budget b, add b edges to non-adjacent vertex pairs in G such that the k-core is maximized.
677	Pivotal Relationship Identification: The K-Truss Minimization Problem	Weijie Zhu, Mengqi Zhang, Chen Chen, Xiaoyang Wang, Fan Zhang, Xuemin Lin	In this paper, we use the k-truss model to measure the stability of a social network. To identify critical connections, we propose a novel problem, named k-truss minimization.
678	Early Discovery of Emerging Entities in Microblogs	Satoshi Akasaki, Naoki Yoshinaga, Masashi Toyoda	We therefore introduce a novel task of discovering truly emerging entities when they have just been introduced to the public through microblogs and propose an effective method based on time-sensitive distant supervision, which exploits distinctive early-stage contexts of emerging entities.
679	Neural Program Induction for KBQA Without Gold Programs or Query Annotations	Ghulam Ahmed Ansari, Amrita Saha, Vishwajeet Kumar, Mohan Bhambhani, Karthik Sankaranarayanan, Soumen Chakrabarti	To deal with these, we propose a noise-resilient NPI model, Stable Sparse Reward based Programmer (SSRP) that evades noise-induced instability through continual retrospection and its comparison with current learning behavior.
680	Medical Concept Representation Learning from Multi-source Data	Tian Bai, Brian L. Egleston, Richard Bleicher, Slobodan Vucetic	To be able to properly utilize such multi-source medical claim data, we propose an approach that represents medical codes from different ontologies in the same vector space.
681	Multi-Domain Sentiment Classification Based on Domain-Aware Embedding and Attention	Yitao Cai, Xiaojun Wan	In this work, we propose a novel completely-shared multi-domain neural sentiment classification model to learn domain-aware word embeddings and make use of domain-aware attention mechanism.
682	A Latent Variable Model for Learning Distributional Relation Vectors	Jose Camacho-Collados, Luis Espinosa-Anke, Shoaib Jameel, Steven Schockaert	To address this issue, we propose a latent variable model that aims to explicitly determine what words from the given sentences best characterize the relationship between the two target words.
683	Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection	Chaotao Chen, Jinhua Peng, Fan Wang, Jun Xu, Hua Wu	In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses.
684	Sentiment-Controllable Chinese Poetry Generation	Huimin Chen, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, Zhipeng Guo	To address this problem, we first collect a manually-labelled sentimental poetry corpus with fine-grained sentiment labels. Then we propose a novel semi-supervised conditional Variational Auto-Encoder model for sentiment-controllable poetry generation.
685	From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots	Shizhe Chen, Qin Jin, Jianlong Fu	In this work, we propose a progressive learning approach for image-pivoted zero-resource machine translation.
686	Learning towards Abstractive Timeline Summarization	Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, Rui Yan	In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events.Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order.To tackle this challenge, we propose a memory-based timeline summarization model (MTS).
687	Coreference Aware Representation Learning for Neural Named Entity Recognition	Zeyu Dai, Hongliang Fei, Ping Li	In this paper, we propose a novel approach to learn coreference-aware word representations for the NER task at the document level.
688	Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction	Yue Deng, Yilin Shen, Hongxia Jin	We introduce an adversarial-critic-and-assistant (ACA) learning framework to improve the performance of existing supervised learning with multiple outputs.
689	End-to-End Multi-Perspective Matching for Entity Resolution	Cheng Fu, Xianpei Han, Le Sun, Bo Chen, Wei Zhang, Suhui Wu, Hao Kong	To resolve the above problems, this paper proposes an end-to-end multi-perspective entity matching model, which can adaptively select optimal similarity measures for heterogenous attributes by jointly learning and selecting similarity measures in an end-to-end way.
690	Difficulty Controllable Generation of Reading Comprehension Questions	Yifan Gao, Lidong Bing, Wang Chen, Michael Lyu, Irwin King	We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG).
691	Modeling Source Syntax and Semantics for Neural AMR Parsing	DongLai Ge, Junhui Li, Muhua Zhu, Shoushan Li	In this paper, we propose two effective approaches to explicitly modeling source syntax and semantics into neural seq2seq AMR parsing.
692	CNN-Based Chinese NER with Lexicon Rethinking	Tao Gui, Ruotian Ma, Qi Zhang, Lujun Zhao, Yu-Gang Jiang, Xuanjing Huang	In this work, we propose a faster alternative to Chinese NER: a convolutional neural network (CNN)-based method that incorporates lexicons using a rethinking mechanism.
693	Dual Visual Attention Network for Visual Dialog	Dan Guo, Hui Wang, Meng Wang	This paper aims to address cross-modal semantic correlation for visual dialog.
694	AmazonQA: A Review-Based Question Answering Task	Mansi Gupta, Nitish Kulkarni, Raghuveer Chanda, Anirudha Rayasam, Zachary C. Lipton	To this end, we introduce a new dataset and propose a method that combines informational retrieval techniques for selecting relevant reviews (given a question) and “reading comprehension” models for synthesizing an answer (given a question and review).
695	Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts	Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael Katz	In this paper, we study the problem of answering questions of type “Could X cause Y?”
696	GSN: A Graph-Structured Network for Multi-Party Dialogues	Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, Rui Yan	This paper generalizes existing sequence-based models to a Graph-Structured neural Network (GSN) for dialogue modeling.
697	Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization	Ting Huang, Gehui Shen, Zhi-Hong Deng	To this end, we propose Leap-LSTM, an LSTM-enhanced model which dynamically leaps between words while reading texts.
698	Relation Extraction Using Supervision from Topic Knowledge of Relation Labels	Haiyun Jiang, Li Cui, Zhe Xu, Deqing Yang, Jindong Chen, Chenguang Li, Jingping Liu, Jiaqing Liang, Chao Wang, Yanghua Xiao, Wei Wang	In this paper, we mine the topic knowledge of a relation to explicitly represent the semantics of this relation, and model relation extraction as a matching problem.
699	Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities	Geewook Kim, Akifumi Okuno, Kazuki Fukui, Hidetoshi Shimodaira	We propose weighted inner product similarity (WIPS) for neural network-based graph embedding.
700	Incorporating Structural Information for Better Coreference Resolution	Fang Kong, Fu Jian	In this paper, we focus on effectively incorporating structural information to neural coreference resolution from three aspects.
701	Knowledge Base Question Answering with Topic Units	Yunshi Lan, Shuohang Wang, Jing Jiang	In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB.
702	Adversarial Transfer for Named Entity Boundary Detection with Pointer Networks	Jing Li, Deheng Ye, Shuo Shang	In this paper, we focus on named entity boundary detection, which aims to detect the start and end boundaries of an entity mention in text, without predicting its type.
703	Towards Discriminative Representation Learning for Speech Emotion Recognition	Runnan Li, Zhiyong Wu, Jia Jia, Yaohua Bu, Sheng Zhao, Helen Meng	In this paper, inspired by human emotion perception, we propose a novel representation learning component (RLC) for SER system, which is constructed with Multi-head Self-attention and Global Context-aware Attention Long Short-Term Memory Recurrent Neutral Network (GCA-LSTM).
704	Self-attentive Biaffine Dependency Parsing	Ying Li, Zhenghua Li, Min Zhang, Rui Wang, Sheng Li, Luo Si	Motivated by the success of the transformer-based machine translation, this work for the first time applies the self-attention mechanism to dependency parsing as the replacement of the BiLSTM-based encoders, leading to competitive performance on both English and Chinese benchmark data.
705	Reading selectively via Binary Input Gated Recurrent Unit	Zhe Li, Peisong Wang, Hanqing Lu, Jian Cheng	Inspired by human reading, we introduce binary input gated recurrent unit (BIGRU), a GRU based model using a binary input gate instead of the reset gate in GRU.
706	Learning to Select Knowledge for Response Generation in Dialog Systems	Rongzhong Lian, Min Xie, Fan Wang, Jinhua Peng, Hua Wu	Motivated by this, we propose an end-to-end neural model which employs a novel knowledge selection mechanism where both prior and posterior distributions over knowledge are used to facilitate knowledge selection.
707	Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Sentiment Classification	Peiqin Lin, Meng Yang, Jianhuang Lai	In this paper, we propose a novel framework for aspect level sentiment classification, deep mask memory network with semantic dependency and context moment (DMMN-SDCM), which integrates semantic parsing information of the aspect and the inter-aspect relation information into deep memory network.
708	Exploring and Distilling Cross-Modal Information for Image Captioning	Fenglin Liu, Xuancheng Ren, Yuanxin Liu, Kai Lei, Xu Sun	In this work, we argue that such understanding requires visual attention to correlated image regions and semantic attention to coherent attributes of interest.
709	Network Embedding with Dual Generation Tasks	Jie Liu, Na Li, Zhicheng He	In this paper, we propose a general end-to-end model, Dual GEnerative Network Embedding (DGENE), to leverage the complementary information of network structure and content.
710	Building Personalized Simulator for Interactive Search	Qianlong Liu, Baoliang Cui, Zhongyu Wei, Baolin Peng, Haikuan Huang, Hongbo Deng, Jianye Hao, Xuanjing Huang, Kam-Fai Wong	To address this issue, we propose to employ a simulator to mimic the environment for the offline training of the agent.
711	A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer	Fuli Luo, Peng Li, Jie Zhou, Pengcheng Yang, Baobao Chang, Xu Sun, Zhifang Sui	Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
712	Unsupervised Neural Aspect Extraction with Sememes	Ling Luo, Xiang Ao, Yan Song, Jinyao Li, Xiaopeng Yang, Qing He, Dong Yu	In this paper, we present an unsupervised neural framework that leverages sememes to enhance lexical semantics.
713	Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning	Chao Ma, F A Rezaur Rahman Chowdhury, Aryan Deshwal, Md Rakibul Islam, Janardhan Rao Doppa, Dan Roth	This paper makes four contributions towards the goal of a computationally-efficient inference and training approach for structured prediction that allows to employ complex models and to optimize for non-decomposable loss functions.
714	Aspect-Based Sentiment Classification with Attentive Neural Turing Machines	Qianren Mao, Jianxin Li, Senzhang Wang, Yuanning Zhang, Hao Peng, Min He, Lihong Wang	To solve this issue, we propose a novel model of Attentive Neural Turing Machines (ANTM).
715	Learning Task-Specific Representation for Novel Words in Sequence Labeling	Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Jinlan Fu, Xuanjing Huang	In this work, we address the OOV problem in sequence labeling using only training data of the task.
716	Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control	Amir Pouran Ben Veyseh, Thien Nguyen, Dejing Dou	This paper introduces a novel method to use dependency trees in RE for deep learning models that jointly predicts dependency and semantics relations.
717	Learn to Select via Hierarchical Gate Mechanism for Aspect-Based Sentiment Analysis	Xiangying Ran, Yuanyuan Pan, Wei Sun, Chongjun Wang	In this paper, we propose a novel architecture named Hierarchical Gate Memory Network (HGMN) for ABSA: firstly, we employ the proposed hierarchical gate mechanism to learn to select the related part about the given aspect, which can keep the original sequence structure of sentence at the same time.
718	Aligning Learning Outcomes to Learning Resources: A Lexico-Semantic Spatial Approach	Swarnadeep Saha, Malolan Chetlur, Tejas Indulal Dhamecha, W M Gayathri K Wijayarathna, Red Mendoza, Paul Gagnon, Nabil Zary, Shantanu Godbole	In this paper, we introduce the novel problem of aligning LOs (LO is usually a sentence long text) to relevant pages of LRs (LRs are in the form of slide decks).
719	A Deep Generative Model for Code Switched Text	Bidisha Samanta, Sharmila Reddy, Hussain Jagirdar, Niloy Ganguly, Soumen Chakrabarti	We introduce VACS, a novel variational autoencoder architecture specifically tailored to code-switching phenomena.
720	Knowledge Aware Semantic Concept Expansion for Image-Text Matching	Botian Shi, Lei Ji, Pan Lu, Zhendong Niu, Nan Duan	In this paper, we develop a Scene Concept Graph (SCG) by aggregating image scene graphs and extracting frequently co-occurred concept pairs as scene common-sense knowledge.
721	Exploiting Persona Information for Diverse Generation of Conversational Responses	Haoyu Song, Wei-Nan Zhang, Yiming Cui, Dong Wang, Ting Liu	In this paper, we propose a memory-augmented architecture to exploit persona information from context and incorporate a conditional variational autoencoder model together to generate diverse and sustainable conversations.
722	Cold-Start Aware Deep Memory Network for Multi-Entity Aspect-Based Sentiment Analysis	Kaisong Song, Wei Gao, Lujun Zhao, Jun Lin, Changlong Sun, Xiaozhong Liu	In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple entities in their context.
723	GANs for Semi-Supervised Opinion Spam Detection	Gray Stanton, Athirai A. Irissappane	We propose spamGAN, a generative adversarial network which relies on limited labeled data as well as unlabeled data for opinion spam detection.
724	PRoFET: Predicting the Risk of Firms from Event Transcripts	Christoph Kilian Theil, Samuel Broscheit, Heiner Stuckenschmidt	We introduce PRoFET, the first neural model for volatility prediction jointly exploiting both semantic language representations and a comprehensive set of financial features.
725	Unsupervised Embedding Enhancements of Knowledge Graphs using Textual Associations	Neil Veira, Brian Keng, Kanchana Padmanabhan, Andreas Veneris	This paper describes an unsupervised approach to incorporate textual information by augmenting entity embeddings with embeddings of associated words.
726	Swell-and-Shrink: Decomposing Image Captioning by Transformation and Summarization	Hanzhang Wang, Hanli Wang, Kaisheng Xu	To overcome the shortcomings, a swell-shrink method is proposed to redefine image captioning as a compositional task which consists of two separated modules: modality transformation and text compression.
727	T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion	Tianming Wang, Xiaojun Wan	In this paper, we present a novel conditional variational autoencoder based on Transformer for missing plot generation.
728	Robust Embedding with Multi-Level Structures for Link Prediction	Zihan Wang, Zhaochun Ren, Chunyu He, Peng Zhang, Yue Hu	In this work, we propose a novel multi-level graph neural network (M-GNN) to address the above challenges.
729	Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation	Yang Wei, Honglei Guo, Jinmao Wei, Zhong Su	In this paper, we propose a multi-grained framework for automatic mind-map generation.
730	Correct-and-Memorize: Learning to Translate from Interactive Revisions	Rongxiang Weng, Hao Zhou, Shujian Huang, Lei Li, Yifan Xia, Jiajun Chen	To solve both issues, we propose CAMIT, a novel method for translating in an interactive environment.
731	Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach	Junshuang Wu, Richong Zhang, Yongyi Mao, Hongyu Guo, Jinpeng Huai	In this paper, we directly model the structured, noisy labels with a novel content-sensitive weighting schema.
732	Mask and Infill: Applying Masked Language Model for Sentiment Transfer	Xing Wu, Tao Zhang, Liangjun Zang, Jizhong Han, Songlin Hu	With this intuition, we propose a two steps approach: Mask and Infill.
733	Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs	Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao	In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations.
734	RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction	Rui Xia, Mengran Zhang, Zixiang Ding	In this work, we propose a joint emotion cause extraction framework, named RNN-Transformer Hierarchical Network (RTHN), to encode and classify multiple clauses synchronously.
735	Sharing Attention Weights for Fast Transformer	Tong Xiao, Yinqiao Li, Jingbo Zhu, Zhengtao Yu, Tongran Liu	In this paper we speed up Transformer via a fast and lightweight attention model.
736	A Goal-Driven Tree-Structured Neural Model for Math Word Problems	Zhipeng Xie, Shichao Sun	This paper proposes a tree-structured neural model to generate expression tree in a goal-driven manner.
737	Dual-View Variational Autoencoders for Semi-Supervised Text Matching	Zhongbin Xie, Shuai Ma	In this study, we propose to take the sentence-level embedding features and the word-level interaction features as two distinct views of a sentence pair, and unify them with a framework of Variational Autoencoders such that the sentence pair is matched in a semi-supervised manner.
738	Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis	Bowen Xing, Lejian Liao, Dandan Song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, Heyan Huang	This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism.
739	Polygon-Net: A General Framework for Jointly Boosting Multiple Unsupervised Neural Machine Translation Models	Chang Xu, Tao Qin, Gang Wang, Tie-Yan Liu	In this work, we propose a general framework called Polygon-Net, which leverages multi auxiliary languages for jointly boosting unsupervised neural machine translation models.
740	Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning	Mengge Xue, Weiming Cai, Jinsong Su, Linfeng Song, Yubin Ge, Yubao Liu, Bin Wang	In this paper, we propose a novel end-to-end neural network with recurrent random-walk layers for collective EL, which introduces external knowledge to model the semantic interdependence between different EL decisions.
741	Robust Audio Adversarial Example for a Physical Attack	Hiromu Yakura, Jun Sakuma	We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world.
742	HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning	Shiyang Yan, Jun Xu, Yuai Liu, Lin Xu	In this paper, instead, we propose a novel hierarchical offshoot recurrent network (HorNet) for improving person re-ID via image captioning.
743	Knowledge-enhanced Hierarchical Attention for Community Question Answering with Multi-task and Adaptive Learning	Min Yang, Lei Chen, Xiaojun Chen, Qingyao Wu, Wei Zhou, Ying Shen	In this paper, we propose a Knowledge-enhanced Hierarchical Attention for community question answering with Multi-task learning and Adaptive learning (KHAMA).
744	Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling	Pengcheng Yang, Fuli Luo, Peng Chen, Lei Li, Zhiyi Yin, Xiaodong He, Xu Sun	Therefore, in this work, we present a commonsense-driven generative model, which aims to introduce crucial commonsense from the external knowledge base for visual storytelling.
745	Triplet Enhanced AutoEncoder: Model-free Discriminative Network Embedding	Yao Yang, Haoran Chen, Junming Shao	In this paper, we present Triplet Enhanced AutoEncoder (TEA), a new deep network embedding approach from the perspective of metric learning.
746	Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax	Yinfei Yang, Gustavo Hernandez Abrego, Steve Yuan, Mandy Guo, Qinlan Shen, Daniel Cer, Yun-hsuan Sung, Brian Strope, Ray Kurzweil	In this paper, we present an approach to learn multilingual sentence embeddings using a bi-directional dual-encoder with additive margin softmax.
747	Utilizing Non-Parallel Text for Style Transfer by Making Partial Comparisons	Di Yin, Shujian Huang, Xin-Yu Dai, Jiajun Chen	In this paper, we propose making partial comparisons to explicitly model the content and style correspondence of instances, respectively.
748	Graph-based Neural Sentence Ordering	Yongjing Yin, Linfeng Song, Jinsong Su, Jiali Zeng, Chulun Zhou, Jiebo Luo	In this paper, we propose a novel and flexible graph-based neural sentence ordering model, which adopts graph recurrent network \citep{Zhang:acl18} to accurately learn semantic representations of the sentences.
749	Refining Word Representations by Manifold Learning	Chu Yonghe, Hongfei Lin, Liang Yang, Yufeng Diao, Shaowu Zhang, Fan Xiaochao	In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifold learning.
750	Beyond Word Attention: Using Segment Attention in Neural Relation Extraction	Bowen Yu, Zhenyu Zhang, Tingwen Liu, Bin Wang, Sujian Li, Quangang Li	In this paper, we aim to incorporate such segment information into neural relation extractor.
751	Adapting BERT for Target-Oriented Multimodal Sentiment Classification	Jianfei Yu, Jing Jiang	Motivated by this observation and inspired by the recently proposed BERT architecture, we study Target-oriented Multimodal Sentiment Classification (TMSC) and propose a multimodal BERT architecture.
752	Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations	Dong Zhang, Liangqing Wu, Changlong Sun, Shoushan Li, Qiaoming Zhu, Guodong Zhou	In this paper, we focus on emotion detection in multi-speaker conversations instead of traditional two-speaker conversations in existing studies.
753	Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model	Junchi Zhang, Yanxia Qin, Yue Zhang, Mengchi Liu, Donghong Ji	We build a first model to this end using a neural transition-based framework, incrementally predicting complex joint structures in a state-transition process.
754	Multi-view Knowledge Graph Embedding for Entity Alignment	Qingheng Zhang, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, Yuzhong Qu	In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment.
755	Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis	Yazhou Zhang, Qiuchi Li, Dawei Song, Peng Zhang, Panpan Wang	In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics.
756	A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots	Xueliang Zhao, Chongyang Tao, Wei Wu, Can Xu, Dongyan Zhao, Rui Yan	We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system.
757	Recurrent Neural Network for Text Classification with Hierarchical Multiscale Dense Connections	Yi Zhao, Yanyan Shen, Junjie Yao	Inspired by the advent of the dense connection pattern in advanced convolutional neural networks, we propose a simple yet effective recurrent architecture, named Hierarchical Mutiscale Densely Connected RNNs (HM-DenseRNNs), which: 1) enables direct access to the hidden states of all preceding recurrent units via dense connections, and 2) organizes multiple densely connected recurrent units into a hierarchical multi-scale structure, where the layers are updated at different scales.
758	RLTM: An Efficient Neural IR Framework for Long Documents	Chen Zheng, Yu Sun, Shengxian Wan, Dianhai Yu	This paper proposes a novel End-to-End neural ranking framework called Reinforced Long Text Matching (RLTM) which matches a query with long documents efficiently and effectively.
759	Dynamically Route Hierarchical Structure Representation to Attentive Capsule for Text Classification	Wanshan Zheng, Zibin Zheng, Hai Wan, Chuan Chen	In this paper, we propose a novel architecture that dynamically routes hierarchical structure feature to attentive capsule, named HAC.
760	Sequence Generation: From Both Sides to the Middle	Long Zhou, Jiajun Zhang, Chengqing Zong, Heng Yu	To alleviate these issues, we propose a synchronous bidirectional sequence generation (SBSG) model which predicts its outputs from both sides to the middle simultaneously.
761	Getting in Shape: Word Embedding SubSpaces	Tianyuan Zhou, João Sedoc, Jordan Rodu	We provide a theoretical framework and guarantees which aid in the understanding of empirical results.
762	A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification	Yan Zhou, Longtao Huang, Tao Guo, Jizhong Han, Songlin Hu	In this paper, inspired by span-based methods in NLP, we propose a simple and effective joint model to conduct extraction and classification at span level rather than token level.
763	Earliest-Completion Scheduling of Contract Algorithms with End Guarantees	Spyros Angelopoulos, Shendan Jin	In this work we show how to optimize the time at which the system reaches a desired performance objective, while maintaining interruptible guarantees throughout the entire execution.
764	Finding Optimal Solutions in HTN Planning – A SAT-based Approach	Gregor Behnke, Daniel Höller, Susanne Biundo	We show how the currently best-performing approach to HTN planning – the translation into propositional logic – can be utilised to find optimal plans.
765	Faster Dynamic Controllability Checking in Temporal Networks with Integer Bounds	Nikhil Bhargava, Brian C. Williams	Our work improves the runtime of checking the dynamic controllability of STNUs with integer bounds to O(min(mn, m sqrt(n) log N) + km + k^2n + kn log n).
766	Regular Decision Processes: A Model for Non-Markovian Domains	Ronen I. Brafman, Giuseppe De Giacomo	We introduce and study Regular Decision Processes (RDPs), a new, compact, factored model for domains with non-Markovian dynamics and rewards.
767	Strong Fully Observable Non-Deterministic Planning with LTL and LTLf Goals	Alberto Camacho, Sheila A. McIlraith	In this paper we introduce novel algorithms to compute so-called strong solutions, that guarantee goal satisfaction even in the absence of fairness.
768	Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks	Steven Carr, Nils Jansen, Ralf Wimmer, Alexandru Serban, Bernd Becker, Ufuk Topcu	We propose a novel method that combines techniques from machine learning and formal verification.
769	Influence of State-Variable Constraints on Partially Observable Monte Carlo Planning	Alberto Castellini, Georgios Chalkiadakis, Alessandro Farinelli	In this paper, we propose the introduction of prior knowledge in the form of (probabilistic) relationships among discrete state-variables, for online planning based on the well-known POMCP algorithm.
770	Online Probabilistic Goal Recognition over Nominal Models	Ramon Fraga Pereira, Mor Vered, Felipe Meneguzzi, Miquel Ramírez	This paper revisits probabilistic, model-based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals.
771	Generalized Potential Heuristics for Classical Planning	Guillem Francès, Augusto B. Corrêa, Cedric Geissmann, Florian Pommerening	In this paper, we show that several interesting planning domains possess compact generalized heuristics that can guide a greedy search in guaranteed polynomial time to the goal, and which work for any instance of the domain.
772	Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search	Thomas Gabor, Jan Peter, Thomy Phan, Christian Meyer, Claudia Linnhoff-Popien	We propose an approach to general subgoal-based temporal abstraction in MCTS.
773	Fair Online Allocation of Perishable Goods and its Application to Electric Vehicle Charging	Enrico H. Gerding, Alvaro Perez-Diaz, Haris Aziz, Serge Gaspers, Antonia Marcu, Nicholas Mattei, Toby Walsh	We consider mechanisms for the online allocation of perishable resources such as energy or computational power.
774	Dynamic logic of parallel propositional assignments and its applications to planning	Andreas Herzig, Frédéric Maris, Julien Vianey	We introduce a dynamic logic with parallel composition and two kinds of nondeterministic composition, exclusive and inclusive.
775	Approximability of Constant-horizon Constrained POMDP	Majid Khonji, Ashkan Jasour, Brian Williams	Our first contribution is a reduction from CC-POMDP to C-POMDP and a novel Integer Linear Programming (ILP) formulation.
776	Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations	Joseph Kim, Christian Muise, Ankit Shah, Shubham Agarwal, Julie Shah	In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces.
777	Partitioning Techniques in LTLf Synthesis	Lucas Martinelli Tabajara, Moshe Y. Vardi	In this work, however, we expose fundamental limitations of partitioning that hinder its effective application to symbolic LTLf synthesis.
778	Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning	Thomy Phan, Thomas Gabor, Robert Müller, Christoph Roch, Claudia Linnhoff-Popien	We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning.
779	A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition	Hangwei Qian, Sinno Jialin Pan, Bingshui Da, Chunyan Miao	In this paper, we propose a novel deep learning model to automatically learn meaningful features including statistical features, temporal features and spatial correlation features for activity recognition in a unified framework.
780	Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning	Jendrik Seipp	We introduce a new method that uses saturated cost partitioning to select patterns and show that it outperforms all existing pattern selection algorithms.
781	Scheduling Jobs with Stochastic Processing Time on Parallel Identical Machines	Richard Stec, Antonin Novak, Premysl Sucha, Zdenek Hanzalek	In this paper, we study a classical parallel machine scheduling problem where the processing time of jobs is given by a normal distribution.
782	On Computational Complexity of Pickup-and-Delivery Problems with Precedence Constraints or Time Windows	Xing Tan, Jimmy Xiangji Huang	That is, we propose a local-search formalism and algorithm for solving PDPC problems in particular.
783	Merge-and-Shrink Task Reformulation for Classical Planning	Álvaro Torralba, Silvan Sievers	In this paper, we represent tasks as factored transition systems (FTS), and use the merge-and-shrink (M&S) framework for task reformulation for optimal and satisficing planning.
784	Steady-State Policy Synthesis for Verifiable Control	Alvaro Velasquez	In this paper, we introduce the Steady-State Policy Synthesis (SSPS) problem which consists of ?nding a stochastic decision-making policy that maximizes expected rewards while satisfying a set of asymptotic behavioral speci?cations.
785	Energy-Efficient Slithering Gait Exploration for a Snake-Like Robot Based on Reinforcement Learning	Zhenshan Bing, Christian Lemke, Zhuangyi Jiang, Kai Huang, Alois Knoll	In this work, we present a novel approach for designing an energy-efficient slithering gait for a snake-like robot using a model-free reinforcement learning (RL) algorithm.
786	The Parameterized Complexity of Motion Planning for Snake-Like Robots	Siddharth Gupta, Guy Sa’ar, Meirav Zehavi	Given a “snake-like” robot with initial and final positions in an environment modeled by a graph, our goal is to decide whether the robot can reach the final position from the initial position without intersecting itself.
787	Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs	Madhu Vankadari, Swagat Kumar, Anima Majumder, Kaushik Das	This paper presents a new GAN-based deep learning framework for estimating absolute scale awaredepth and ego motion from monocular images using a completely unsupervised mode of learning.
788	Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos	Haofei Xu, Jianmin Zheng, Jianfei Cai, Juyong Zhang	In this paper, we propose a new learning based method consisting of DepthNet, PoseNet and Region Deformer Networks (RDN) to estimate depth from unconstrained monocular videos without ground truth supervision.
789	Statistical Guarantees for the Robustness of Bayesian Neural Networks	Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, Matthew Wicker	We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two.
790	Lifted Message Passing for Hybrid Probabilistic Inference	Yuqiao Chen, Nicholas Ruozzi, Sriraam Natarajan	In this work, we consider the problem of lifted inference in MLNs with continuous or both discrete and continuous groundings.
791	Bayesian Parameter Estimation for Nonlinear Dynamics Using Sensitivity Analysis	Yi Chou, Sriram Sankaranarayanan	We investigate approximate Bayesian inference techniques for nonlinear systems described by ordinary differential equation (ODE) models.
792	Thompson Sampling on Symmetric Alpha-Stable Bandits	Abhimanyu Dubey, Alex `Sandy’ Pentland	In this paper, we revisit the Thompson Sampling algorithm under rewards drawn from symmetric alpha-stable distributions, which are a class of heavy-tailed probability distributions utilized in finance and economics, in problems such as modeling stock prices and human behavior.
793	On Constrained Open-World Probabilistic Databases	Tal Friedman, Guy Van den Broeck	We propose overcoming these issues by using constraints to restrict this open world.
794	An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph	Dongxiao He, Wenze Song, Di Jin, Zhiyong Feng, Yuxiao Huang	This paper integrates LDA into MRF to form an end-to-end learning system where their parameters can be trained jointly.
795	Exact Bernoulli Scan Statistics using Binary Decision Diagrams	Masakazu Ishihata, Takanori Maehara	In this study, we restrict our attention to the case that the number of data points is moderately small (e.g., 50), the outcome is binary, and the underlying combinatorial structure is represented by a zero-suppressed binary decision diagram (ZDD), and consider the problem of computing the p-value of the combinatorial scan statistics exactly.
796	Hyper-parameter Tuning under a Budget Constraint	Zhiyun Lu, Liyu Chen, Chao-Kai Chiang, Fei Sha	We formulate the task into a sequential decision making problem and propose a solution, which uses a Bayesian belief model to predict future performances, and an action-value function to plan and select the next configuration to run.
797	Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models	Tahrima Rahman, Shasha Jin, Vibhav Gogate	In this paper, we seek to further explore this trade-off between generalization performance and inference accuracy by proposing a novel, partially tractable representation called cutset Bayesian networks (CBNs).
798	Ranked Programming	Tjitze Rienstra	In this paper we combine probabilistic programming methodology with ranking theory and develop a ranked programming language.
799	ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation	Jing Song, Hong Shen, Zijing Ou, Junyi Zhang, Teng Xiao, Shangsong Liang	In this paper, we propose a novel model, Interest Shift and Latent Factors Combination Model (ISLF), which can capture the user’s main intention by taking into account the user’s interest shift (i.e. long-term and short-term interest) and latent factors simultaneously.
800	DiffChaser: Detecting Disagreements for Deep Neural Networks	Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li	This paper proposes DiffChaser, an automated black-box testing framework to detect untargeted/targeted disagreements between version variants of a DNN.
801	SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks	Alireza Abedin, S. Hamid Rezatofighi, Qinfeng Shi, Damith C. Ranasinghe	In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data.
802	Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour	Andrea Aler Tubella, Andreas Theodorou, Frank Dignum, Virginia Dignum	In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs.
803	Decision Making for Improving Maritime Traffic Safety Using Constraint Programming	Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau	To achieve this, we a) formalize the decision model for traffic hotspot mitigation including realistic maritime navigational features and constraints through consultations with domain experts; and b) develop a constraint programming based scheduling approach to mitigate hotspots.
804	Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively	Serena Booth, Christian Muise, Julie Shah	We find that domain, formula size, and negated logical connectives significantly affect comprehension while formula properties typically associated with interpretability are not strong predictors of human ability to comprehend the theory.
805	AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion	Katia Bourahmoune, Toshiyuki Amagasa	We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance.
806	Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization under Uncertainty	Jonathan Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau	To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning.
807	Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation	Dawei Cheng, Yi Tu, Zhenwei Ma, Zhibin Niu, Liqing Zhang	In this paper, we propose a high-order graph attention representation method (HGAR) to learn the embedding of guarantee networks.
808	PI-Bully: Personalized Cyberbullying Detection with Peer Influence	Lu Cheng, Jundong Li, Yasin Silva, Deborah Hall, Huan Liu	In this paper, we propose a personalized cyberbullying detection framework, PI-Bully, that draws on empirical findings from psychology highlighting unique characteristics of victims and bullies and peer influence from like-minded users as predictors of cyberbullying behaviors.
809	The Price of Local Fairness in Multistage Selection	Vitalii Emelianov, George Arvanitakis, Nicolas Gast, Krishna Gummadi, Patrick Loiseau	In this paper we study fairness in k-stage selection problems where additional features are observed at every stage.
810	Enhancing Stock Movement Prediction with Adversarial Training	Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, Tat-Seng Chua	The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model.
811	Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning	Marcello Fiducioso, Sebastian Curi, Benedikt Schumacher, Markus Gwerder, Andreas Krause	We use Safe Contextual Bayesian Optimization to optimize the PID parameters without human intervention.
812	DDL: Deep Dictionary Learning for Predictive Phenotyping	Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun	To address this label-insufficient challenge, we propose a deep dictionary learning framework (DDL) for phenotyping, which utilizes unlabeled data as a complementary source of information to generate a better, more succinct data representation.
813	Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning	Supriyo Ghosh, Jing Yu Koh, Patrick Jaillet	To bridge this gap, we propose a dynamic bike repositioning approach based on a probabilistic satisficing method which uses the uncertain demand parameters that are learnt from historical data.
814	mdfa: Multi-Differential Fairness Auditor for Black Box Classifiers	Xavier Gitiaux, Huzefa Rangwala	This paper presents a new tool, mdfa that identifies the characteristics of the victims of a classifier’s discrimination.
815	CounterFactual Regression with Importance Sampling Weights	Negar Hassanpour, Russell Greiner	In this work, we borrow ideas from domain adaptation to address the distributional shift between the source (outcome of the administered treatment, appearing in the observed training data) and target (outcome of the alternative treatment) that exists due to selection bias.
816	MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals	Shenda Hong, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun	In this work, we propose MultIlevel kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG signals with intuitive explanation aligned with medical knowledge.
817	RDPD: Rich Data Helps Poor Data via Imitation	Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun	To deploy a competitive model in a poor-data environment without requiring direct access to multi-modal data acquired from a rich-data environment, this paper develops and presents a knowledge distillation (KD) method (RDPD) to enhance a predictive model trained on poor data using knowledge distilled from a high-complexity model trained on rich, private data.
818	Systematic Conservation Planning for Sustainable Land-use Policies: A Constrained Partitioning Approach to Reserve Selection and Design.	Dimitri Justeau-Allaire, Philippe Vismara, Philippe Birnbaum, Xavier Lorca	This paper introduces a partitioning approach based on Constraint Programming (CP) for the reserve selection and design problem, dealing with both coverage and complex spatial constraints.
819	Truly Batch Apprenticeship Learning with Deep Successor Features	Donghun Lee, Srivatsan Srinivasan, Finale Doshi-Velez	We introduce a novel apprenticeship learning algorithm to learn an expert’s underlying reward structure in off-policy model-free batch settings.
820	Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks	Jinning Li, Yexiang Xue	We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles.
821	Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies	Muhammad Masood, Finale Doshi-Velez	In this work, we formalize the difference between policies as a difference between the distribution of trajectories induced by each policy, which encourages diversity with respect to both state visitation and action choices.
822	KitcheNette: Predicting and Ranking Food Ingredient Pairings using Siamese Neural Network	Donghyeon Park, Keonwoo Kim, Yonggyu Park, Jungwoon Shin, Jaewoo Kang	In this work, we propose KitcheNette which is a model that predicts food ingredient pairing scores and recommends optimal ingredient pairings.
823	MNN: Multimodal Attentional Neural Networks for Diagnosis Prediction	Zhi Qiao, Xian Wu, Shen Ge, Wei Fan	To increase the robustness towards noisy data, we introduce textual clinical notes in addition to medical codes.
824	Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance	Wenjie Ruan, Min Wu, Youcheng Sun, Xiaowei Huang, Daniel Kroening, Marta Kwiatkowska	We define global robustness as an expectation of the maximal safe radius over a test dataset, and develop an algorithm to approximate the global robustness measure by iteratively computing its lower and upper bounds.
825	Pre-training of Graph Augmented Transformers for Medication Recommendation	Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun	To address these challenges, we propose G-BERT, a new model to combine the power of Graph Neural Networks (GNNs) and BERT (Bidirectional Encoder Representations from Transformers) for medical code representation and medication recommendation.
826	Three-quarter Sibling Regression for Denoising Observational Data	Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich	We present a technique called ‘three-quarter sibling regression’ to partially overcome this limitation.
827	Daytime Sleepiness Level Prediction Using Respiratory Information	Kazuhiko Shinoda, Masahiko Yoshii, Hayato Yamaguchi, Hirotaka Kaji	In this paper, we present the first step towards the continuous sleepiness tracking in daily living situations.
828	Simultaneous Prediction Intervals for Patient-Specific Survival Curves	Samuel Sokota, Ryan D’Orazio, Khurram Javed, Humza Haider, Russell Greiner	In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results.
829	Controllable Neural Story Plot Generation via Reward Shaping	Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, Mark O. Riedl	We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model toward a given goal.
830	Bidirectional Active Learning with Gold-Instance-Based Human Training	Feilong Tang	In this paper, we propose a Bidirectional Active Learning with human Training (BALT) model that can enhance human related expertise during labeling and improve relabelingquality accordingly.
831	Group-Fairness in Influence Maximization	Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick	Drawing on legal and game-theoretic concepts, we introduce formal definitions of fairness in influence maximization.
832	Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving	Akifumi Wachi	We propose a method for efficiently finding failure scenarios; this method trains the adversarial agents using multi-agent reinforcement learning such that the tested rule-based agent fails.
833	Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses	Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Chin	We propose Defense Efficiency Score (DES), a comprehensive metric that measures the gain in unsuccessful attack attempts at the cost of drop in test accuracy of any defense.
834	Who Should Pay the Cost: A Game-theoretic Model for Government Subsidized Investments to Improve National Cybersecurity	Xinrun Wang, Bo An, Hau Chan	To tackle the government’s allocation problem, we introduce a Stackelberg game-theoretic model where the government first commits to an allocation and the companies/users and attacker simultaneously determine their protection and attack (pure or mixed) strategies, respectively.
835	Automatic Grassland Degradation Estimation Using Deep Learning	Xiyu Yan, Yong Jiang, Shuai Chen, Zihao He, Chunmei Li, Shu-Tao Xia, Tao Dai, Shuo Dong, Feng Zheng	Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge Loss to alleviate the class imbalance of semantics in the training stage. 2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation.
836	Balanced Ranking with Diversity Constraints	Ke Yang, Vasilis Gkatzelis, Julia Stoyanovich	In this paper we study this phenomenon using datasets that comprise multiple sensitive attributes.
837	A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data	Mingyang Zhang, Tong Li, Hongzhi Shi, Yong Li, Pan Hui	In this paper, we propose a decomposing approach to address these two challenges.
838	Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields	Yue Zhang, Arti Ramesh	In this work, we present an asynchronous deep reinforcement learning algorithm to automatically learn HL-MRF clause structures.
839	K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection	Yuxi Zhou, Shenda Hong, Junyuan Shang, Meng Wu, Qingyun Wang, Hongyan Li, Junqing Xie	In this paper, we propose a K-margin-based Residual-Convolution-Recurrent neural network (K-margin-based RCR-net) for AF detection from noisy ECGs.
840	LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning	Alberto Camacho, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, Sheila A. McIlraith	We propose using reward machines (RMs), which are automata-based representations that expose reward function structure, as a normal form representation for reward functions.
841	Playgol: Learning Programs Through Play	Andrew Cropper	We introduce the analogous idea of learning programs through play.
842	Learning Relational Representations with Auto-encoding Logic Programs	Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel	This paper introduces a novel framework for relational representation learning that combines the best of both worlds.
843	A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning	Sebastijan Dumancic, Alberto Garcia-Duran, Mathias Niepert	In this work, we compare distributional and symbolic relational learning approaches on various standard relational classification and knowledge base completion tasks.
844	Learning Hierarchical Symbolic Representations to Support Interactive Task Learning and Knowledge Transfer	James R. Kirk, John E. Laird	We present a learning strategy embodied in an ITL agent that interactively learns in one shot the meaning of task concepts for 40 games and puzzles in ambiguous scenarios.
845	EL Embeddings: Geometric Construction of Models for the Description Logic EL++	Maxat Kulmanov, Wang Liu-Wei, Yuan Yan, Robert Hoehndorf	We address the problem of finding vector space embeddings for theories in the Description Logic ??
846	How Well Do Machines Perform on IQ tests: a Comparison Study on a Large-Scale Dataset	Yusen Liu, Fangyuan He, Haodi Zhang, Guozheng Rao, Zhiyong Feng, Yi Zhou	To address this issue, we create IQ10k, a large-scale dataset that contains more than 10,000 IQ test questions.
847	Synthesizing Datalog Programs using Numerical Relaxation	Xujie Si, Mukund Raghothaman, Kihong Heo, Mayur Naik	In this paper, we present Difflog, a technique to extend the logic programming language Datalog to the continuous setting.