Paper Digest: IJCAI 2020 Highlights

July 20, 2020August 18, 2020 admin

Download IJCAI-2020-Paper-Digests.pdf– highlights of all IJCAI-2020 papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords and find related papers and patents.

International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. In 2020, the conference will be postponed to January 2021 due to Covid-19 pandemic.

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TABLE 1: IJCAI 2020 Papers

	Title	Authors	Highlight
1	An Algorithm for Multi-Attribute Diverse Matching	Saba Ahmadi, Faez Ahmed, John P. Dickerson, Mark Fuge, Samir Khuller	In this work, we prove that the problem of simultaneously maximizing diversity along several features (e.g., country of citizenship, gender, skills) is NP-hard.
2	Intention Progression under Uncertainty	Yuan Yao, Natasha Alechina, Brian Logan, John Thangarajah	In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain.
3	Social Ranking Manipulability for the CP-Majority, Banzhaf and Lexicographic Excellence Solutions	Tahar Allouche, Bruno Escoffier, Stefano Moretti, Meltem Öztürk	In this paper, we focus on rules representing three classical approaches in social choice theory: the marginal contribution approach, the lexicographic approach and the (ceteris paribus) majority one.
4	Maximum Nash Welfare and Other Stories About EFX	Georgios Amanatidis, Georgios Birmpas, Aris Filos-Ratsikas, Alexandros Hollender, Alexandros A. Voudouris	We consider the classic problem of fairly allocating indivisible goods among agents with additive valuation functions and explore the connection between two prominent fairness notions: maximum Nash welfare (MNW) and envy-freeness up to any good (EFX).
5	Speeding Up Incomplete GDL-based Algorithms for Multi-agent Optimization with Dense Local Utilities	Yanchen Deng, Bo An	In this paper, we present several novel sorting-based acceleration algorithms by alleviating the effect of densely distributed local utilities.
6	Almost Group Envy-free Allocation of Indivisible Goods and Chores	Haris Aziz, Simon Rey	For two natural classes of additive utilities, we design polynomial-time algorithms to compute a GEF1 allocation.
7	Uniform Welfare Guarantees Under Identical Subadditive Valuations	Siddharth Barman, Ranjani G. Sundaram	We study the problem of allocating indivisible goods among agents that have an identical subadditive valuation over the goods.
8	Biased Opinion Dynamics: When the Devil is in the Details	Aris Anagnostopoulos, Luca Becchetti, Emilio Cruciani, Francesco Pasquale, Sara Rizzo	The framework we propose exhibits a rich structure, with a nonobvious interplay between topology and underlying update rule.
9	Formalizing Group and Propagated Trust in Multi-Agent Systems	Nagat Drawel, Jamal Bentahar, Amine Laarej, Gaith Rjoub	We present a formal framework that allows individual and group of agents to reason about their trust toward other agents.
10	Fine-Grained View on Bribery for Group Identification	Niclas Boehmer, Robert Bredereck, Dušan Knop, Junjie Luo	Complementing previous results showing polynomial-time solvability or NP-hardness of bribery for various social rules in the constructive (aiming at making specific individuals socially qualified) or destructive (aiming at making specific individuals socially disqualified) setting, we provide a comprehensive picture of the parameterized computational complexity landscape.
11	Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise	Ioannis Caragiannis, Christos Kaklamanis, Nikos Karanikolas, George A. Krimpas	To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees.
12	Decentralized MCTS via Learned Teammate Models	Aleksander Czechowski, Frans A. Oliehoek	In this paper, we present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search, combined with models of teammates learned from previous episodic runs.
13	On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules	Chinmay Sonar, Palash Dey, Neeldhara Misra	In this work, we resolve the complexity of both problems for the Chamberlin-Courant and Monroe voting rules in the settings of rankings as well as approval ballots.
14	Stable Roommate Problem with Diversity Preferences	Niclas Boehmer, Edith Elkind	We study the complexity of finding good allocations of agents to rooms under the assumption that agents have diversity preferences (Bredereck, Elkind, Igarashi, AAMAS’19): each agent belongs to one of the two types (e.g., juniors and seniors, artists and engineers), and agents’ preferences over rooms depend solely on the fraction of agents of their own type among their potential roommates.
15	Strategic Campaign Management in Apportionment Elections	Robert Bredereck, Piotr Faliszewski, Michal Furdyna, Andrzej Kaczmarczyk, Martin Lackner	We study the complexity of the following bribery-style problem: Given the distribution of votes among the parties, what is the smallest number of voters that need to be convinced to vote for our party, so that it gets a desired number of seats.
16	Concentration of Distortion: The Value of Extra Voters in Randomized Social Choice	Brandon Fain, William Fan, Kamesh Munagala	We study higher statistical moments of Distortion for randomized social choice in a metric implicit utilitarian model.
17	Assume-Guarantee Synthesis for Prompt Linear Temporal Logic	Nathanaël Fijalkow, Bastien Maubert, Aniello Murano, Moshe Vardi	We study assume-guarantee synthesis for prompt-LTL: the goal is to construct a system such that for all environments satisfying a first prompt-LTL formula (the assumption) the system composed with this environment satisfies a second prompt-LTL formula (the guarantee). This problem has been open for a decade.
18	Peer-Prediction in the Presence of Outcome Dependent Lying Incentives	Naman Goel, Aris Filos-Ratsikas, Boi Faltings	We derive conditions under which a peer-consistency mechanism can be used to elicit truthful data from non-trusted rational agents when an aggregate statistic of the collected data affects the amount of their incentives to lie.
19	Proportionality in Approval-Based Elections With a Variable Number of Winners	Rupert Freeman, Anson Kahng, David M. Pennock	We study proportionality in approval-based multiwinner elections with a variable number of winners, where both the size and identity of the winning committee are informed by voters’ opinions.
20	Flow-Based Network Creation Games	Hagen Echzell, Tobias Friedrich, Pascal Lenzner, Anna Melnichenko	We introduce Flow-Based NCGs where the selfish agents focus on bandwidth instead of latency.
21	Stable Matchings with Diversity Constraints: Affirmative Action is beyond NP	Jiehua Chen, Robert Ganian, Thekla Hamm	We investigate the following many-to-one stable matching problem with diversity constraints (SMTI-DIVERSE): Given a set of students and a set of colleges which have preferences over each other, where the students have overlapping types, and the colleges each have a total capacity as well as quotas for individual types (the diversity constraints), is there a matching satisfying all diversity constraints such that no unmatched student-college pair has an incentive to deviate?
22	Mechanism Design for School Choice with Soft Diversity Constraints	Haris Aziz, Serge Gaspers, Zhaohong Sun	Our central contribution is presenting a new class of algorithms that takes into account the representations of combinations of student types.
23	Maximizing Welfare with Incentive-Aware Evaluation Mechanisms	Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Jack Z. Wang	Motivated by applications such as college admission and insurance rate determination, we study a classification problem where the inputs are controlled by strategic individuals who can modify their features at a cost.
24	Ethics, Prosperity, and Society: Moral Evaluation Using Virtue Ethics and Utilitarianism	Aditya Hegde, Vibhav Agarwal, Shrisha Rao	This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism.
25	Prophet Inequalities for Bayesian Persuasion	Niklas Hahn, Martin Hoefer, Rann Smorodinsky	We study an information-structure design problem (i.e., a Bayesian persuasion problem) in an online scenario.
26	Fair Division of Time: Multi-layered Cake Cutting	Hadi Hosseini, Ayumi Igarashi, Andrew Searns	We initiate the study of multi-layered cake cutting with the goal of fairly allocating multiple divisible resources (layers of a cake) among a set of agents.
27	Well-Structured Committees	Sushmita Gupta, Pallavi Jain, Saket Saurabh	Motivated by possible interactions and dependencies between candidates, we study a generalization of committee selection in which the candidates are connected via a network and the task is to select a committee that relates to the given votes while also satisfy certain properties with respect to this candidate network.
28	Evaluating Committees for Representative Democracies: the Distortion and Beyond	Micha? Jaworski, Piotr Skowron	We study a model where a group of representatives is elected to make a series of decisions on behalf of voters.
29	The Complexity of Election Problems with Group-Separable Preferences	Piotr Faliszewski, Alexander Karpov, Svetlana Obraztsova	We analyze the complexity of several NP-hard election-related problems under the assumptions that the voters have group-separable preferences
30	Logics of Allies and Enemies: A Formal Approach to the Dynamics of Social Balance Theory	Wiebe Van der Hoek, Louwe Kuijer, Yì Wáng	We combine social balance theory with temporal logic to obtain a Logic of Allies and Enemies (LAE), which formally describes the likely changes to a social network due to social pressure.
31	Synthesis of Deceptive Strategies in Reachability Games with Action Misperception	Abhishek N. Kulkarni, Jie Fu	To answer this question, we introduce a dynamic hypergame model to capture the reachability game with evolving misperception of P2.
32	Selecting Voting Locations for Fun and Profit	Zack Fitzsimmons, Omer Lev	The most well known in the media is gerrymandering, in which district border-lines are changed to increase a party’s chance to win, but a different geographical manipulation involves influencing the election by selecting the location of polling places, as many people are not willing to go to any distance to vote. In this paper we initiate the study of this manipulation.
33	Incentive-Compatible Diffusion Auctions	Bin Li, Dong Hao, Dengji Zhao	In this work, we identify a sufficient and necessary condition for all dominant-strategy incentive-compatible (DSIC) diffusion auctions.
34	Strategyproof Mechanism for Two Heterogeneous Facilities with Constant Approximation Ratio	Minming Li, Pinyan Lu, Yuhao Yao, Jialin Zhang	In this paper, we study the two-facility location game with optional preference where the acceptable set of facilities for each agent could be different and an agent’s cost is his distance to the closest facility within his acceptable set.
35	Dinkelbach-Type Algorithm for Computing Quantal Stackelberg Equilibrium	Jakub Cerny, Viliam Lisý, Branislav Bošanský, Bo An	We (1) present a transformation of the primal problem for computing QSE using a Dinkelbach’s method for any general-sum normal-form game, (2) provide a gradient-based and a MILP-based algorithm, give the convergence criteria, and bound their error, and finally (3) we experimentally demonstrate that using our novel transformation, a QSE can be closely approximated several orders of magnitude faster.
36	A Penny for Your Thoughts: The Value of Communication in Ad Hoc Teamwork	Reuth Mirsky, William Macke, Andy Wang, Harel Yedidsion, Peter Stone	The goal of this work is enabling improved ad hoc teamwork by judiciously leveraging the ability of the team to communicate.
37	Convexity of b-matching Games	Soh Kumabe, Takanori Maehara	In this study, we give a necessary and sufficient condition of the convexity of the b-matching game.
38	Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs	Saaduddin Mahmud, Md. Mosaddek Khan, Moumita Choudhury, Long Tran-Thanh, Nicholas R. Jennings	In this paper, we combine both of these frameworks into the Mixed Integer Functional DCOP (MIF-DCOP) framework that can deal with problems regardless of their variables’ type.
39	Tight Approximation for Proportional Approval Voting	Szymon Dudycz, Pasin Manurangsi, Jan Marcinkowski, Krzysztof Sornat	In this paper, we study approximability of Thiele rules, which are known to be NP-hard to solve exactly.
40	Partial Adversarial Behavior Deception in Security Games	Thanh H. Nguyen, Arunesh Sinha, He He	We study attacker behavior deception with three main contributions.
41	Approximate Pareto Set for Fair and Efficient Allocation: Few Agent Types or Few Resource Types	Trung Thanh Nguyen, Jörg Rothe	We solve this problem approximately in polynomial time by modeling it as a bi-criteria optimization problem that can be solved efficiently by determining an approximate Pareto set of bounded size.
42	A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation	Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis	We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets.
43	Computational Aspects of Conditional Minisum Approval Voting in Elections with Interdependent Issues	Evangelos Markakis, Georgios Papasotiropoulos	Motivated by this, we focus on the computational aspects of Conditional Minisum, where progress has been rather scarce so far. We identify restrictions to every voter’s dependencies, under which we provide the first multiplicative approximation algorithms for the problem.
44	Combining Direct Trust and Indirect Trust in Multi-Agent Systems	Elham Parhizkar, Mohammad Hossein Nikravan, Robert C. Holte, Sandra Zilles	This paper provides the first systematic study on when it is beneficial to combine these two types of trust as opposed to relying on only one of them.
45	Keeping Your Friends Close: Land Allocation with Friends	Edith Elkind, Neel Patel, Alan Tsang, Yair Zick	We examine the problem of assigning plots of land to prospective buyers who prefer living next to their friends.
46	Verifying Fault-Tolerance in Probabilistic Swarm Systems	Alessio Lomuscio, Edoardo Pirovano	We present a method for reasoning about fault-tolerance in unbounded robotic swarms.
47	Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs	Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm	We present a polynomial time algorithm for optimizing one two-part tariff.
48	Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach	Yujian Ye, Dawei Qiu, Jonathan Ward, Marcin Abram	This paper showcases the first application of deep reinforcement learning (DRL) to real-time autonomous energy management for a multi-carrier energy system.
49	Altruism in Coalition Formation Games	Anna Maria Kerkmann, Jörg Rothe	Nguyen et al. [2016] introduced altruistic hedonic games in which agents’ utilities depend not only on their own preferences but also on those of their friends in the same coalition. We propose to extend their model to coalition formation games in general, considering also the friends in other coalitions.
50	A Multi-Objective Approach to Mitigate Negative Side Effects	Sandhya Saisubramanian, Ece Kamar, Shlomo Zilberstein	We formulate the problem of mitigating the impact of NSE as a multi-objective Markov decision process with lexicographic reward preferences and slack.
51	The Competitive Effects of Variance-based Pricing	Ludwig Dierks, Sven Seuken	In this paper, we study a variance-based pricing rule in a two-provider market setting and perform a game-theoretic analysis of the resulting competitive effects.
52	When to Follow the Tip: Security Games with Strategic Informants	Weiran Shen, Weizhe Chen, Taoan Huang, Rohit Singh, Fei Fang	In this paper, we introduce a new player — a strategic informant, who can observe and report upcoming attacks — to the defender-attacker security game setting.
53	Competition Among Contests: a Safety Level Analysis	Ron Lavi, Omer Shiran-Shvarzbard	We study a competition among two contests, where each contest designer aims to attract as much effort as possible.
54	Participatory Budgeting with Project Interactions	Pallavi Jain, Krzysztof Sornat, Nimrod Talmon	We study the computational complexity of finding bundles that maximize voter utility, as defined with respect to such functions.
55	PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection	Nicholas Mattei, Paolo Turrini, Stanislav Zhydkov	Here, we present a novel algorithm for impartial peer selection, PeerNomination, and provide a theoretical analysis of its accuracy.
56	Budgeted Facility Location Games with Strategic Facilities	Minming Li, Chenhao Wang, Mengqi Zhang	This paper studies the facility location games with payments, where facilities are strategic players.
57	Monte-Carlo Tree Search for Scalable Coalition Formation	Feng Wu, Sarvapali D. Ramchurn	We propose a novel algorithm based on Monte-Carlo tree search for the problem of coalition structure generation (CSG).
58	Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning	Ying Wen, Yaodong Yang, Jun Wang	In this paper, we introduce generalized recursive reasoning (GR2) as a novel framework to model agents with different \emph{hierarchical} levels of rationality; our framework enables agents to exhibit varying levels of “thinking” ability thereby allowing higher-level agents to best respond to various less sophisticated learners.
59	Sybil-proof Answer Querying Mechanism	Yao Zhang, Xiuzhen Zhang, Dengji Zhao	We study a question answering problem on a social network, where a requester is seeking an answer from the agents on the network.
60	WEFE: The Word Embeddings Fairness Evaluation Framework	Pablo Badilla, Felipe Bravo-Marquez, Jorge Pérez	In this paper we propose WEFE, the word embeddings fairness evaluation framework, to encapsulate, evaluate and compare fairness metrics.
61	Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness	Samuel Yeom, Matt Fredrikson	Our contributions are twofold: First, we introduce the definition of a minimal metric and characterize the behavior of models in terms of minimal metrics. Second, for more complicated models, we apply the mechanism of randomized smoothing from adversarial robustness to make them individually fair under a given weighted Lp metric.
62	Achieving Outcome Fairness in Machine Learning Models for Social Decision Problems	Boli Fang, Miao Jiang, Pei-yi Cheng, Jerry Shen, Yi Fang	In view of such an issue, we propose in this paper the strategy of fairgroups, based on the legal doctrine of disparate impact, to improve fairness in prediction outcomes.
63	Relation-Based Counterfactual Explanations for Bayesian Network Classifiers	Emanuele Albini, Antonio Rago, Pietro Baroni, Francesca Toni	We propose a general method for generating counterfactual explanations (CFXs) for a range of Bayesian Network Classifiers (BCs), e.g. single- or multi-label, binary or multidimensional.
64	Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models	Pingchuan Ma, Shuai Wang, Jin Liu	In this paper, we propose a novel framework employing metamorphic testing, a well-established software testing scheme, to test NLP models and find discriminatory inputs that provoke fairness violations.
65	Channel-Level Variable Quantization Network for Deep Image Compression	Zhisheng Zhong, Hiroaki Akutsu, Kiyoharu Aizawa	In this paper, we propose a channel-level variable quantization network to dynamically allocate more bitrates for significant channels and withdraw bitrates for negligible channels.
66	Disentangled Feature Learning Network for Vehicle Re-Identification	Yan Bai, Yihang Lou, Yongxing Dai, Jun Liu, Ziqian Chen, Ling-Yu Duan	Here we propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently, which are discriminative at details and invariant to orientations, respectively.
67	EViLBERT: Learning Task-Agnostic Multimodal Sense Embeddings	Agostina Calabrese, Michele Bevilacqua, Roberto Navigli	To address this issue, we introduce EViLBERT, an approach which is able to perform image classification over an open set of concepts, both concrete and non-concrete.
68	Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos	Haimei Zhao, Wei Bian, Bo Yuan, Dacheng Tao	Therefore, we present a collaborative learning framework, consisting of DepthNet, LocalPoseNet and GlobalPoseNet with a joint optimization loss to estimate depth, VO and camera localization unitedly.
69	A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time	Elena Burceanu, Marius Leordeanu	We formulate object segmentation in video as a spectral graph clustering problem in space and time, in which nodes are pixels and their relations form local neighbourhoods.
70	Reference Guided Face Component Editing	Qiyao Deng, Jie Cao, Yunfan Liu, Zhenhua Chai, Qi Li, Zhenan Sun	To break the limitations (e.g. shape, mask or sketch) of the existing methods, we propose a novel framework termed r FACE (Reference Guided FAce Component Editing) for diverse and controllable face component editing with geometric changes.
71	When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark	Xiao Wang, Jun Chen, Zheng Wang, Wu Liu, Shin’ichi Satoh, Chao Liang, Chia-Wen Lin	In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: NightSurveillance1.
72	TextFuseNet: Scene Text Detection with Richer Fused Features	Jian Ye, Zhe Chen, Juhua Liu, Bo Du	Unlike existing text detection approaches that only perceive texts based on limited feature representations, we propose a novel framework, namely TextFuseNet, to exploit the use of richer features fused for text detection.
73	SelectScale: Mining More Patterns from Images via Selective and Soft Dropout	Zhengsu Chen, Jianwei Niu, Xuefeng Liu, Shaojie Tang	To tackle this problem, we propose SelectScale. Instead of randomly dropping units, SelectScale selects the important features in networks and adjusts them during training.
74	GSM: Graph Similarity Model for Multi-Object Tracking	Qiankun Liu, Qi Chu, Bin Liu, Nenghai Yu	In this paper, we focus on leveraging the relations among objects to improve robustness of the similarity model.
75	Deep Interleaved Network for Single Image Super-Resolution with Asymmetric Co-Attention	Feng Li, Runmin Cong, Huihui Bai, Yifan He	In this paper, to tackle this problem, we propose a deep interleaved network (DIN) to learn how information at different states should be combined for image SR where shallow information guides deep representative features prediction.
76	Meta Segmentation Network for Ultra-Resolution Medical Images	Tong Wu, Bicheng Dai, Shuxin Chen, Yanyun Qu, Yuan Xie	However, the fusion structure in these methods require to be designed elaborately to achieve desirable result, which leads to model redundancy. In this paper, we propose Meta Segmentation Network (MSN) to solve this challenging problem.
77	Lifelong Zero-Shot Learning	Kun Wei, Cheng Deng, Xu Yang	In this paper, we propose a new ZSL setting, named as Lifelong Zero-Shot Learning (LZSL), which aims to accumulate the knowledge during the learning from multiple datasets and recognize unseen classes of all trained datasets.
78	Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data	Haytham M. Fayek, Anurag Kumar	In this paper, we advocate that sound recognition is inherently a multi-modal audiovisual task in that it is easier to differentiate sounds using both the audio and visual modalities as opposed to one or the other.
79	JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks	Menglu Wang, Xueyang Fu, Zepei Sun, Zheng-Jun Zha	To solve this problem, we propose compression quality ranker-guided networks for this specific JPEG artifacts removal.
80	SimPropNet: Improved Similarity Propagation for Few-shot Image Segmentation	Siddhartha Gairola, Mayur Hemani, Ayush Chopra, Balaji Krishnamurthy	In this work, we demonstrate gaps in the utilization of this similarity information in existing methods, and present a framework – SimPropNet, to bridge those gaps.
81	Multi-graph Fusion for Functional Neuroimaging Biomarker Detection	Jiangzhang Gan, Xiaofeng Zhu, Rongyao Hu, Yonghua Zhu, Junbo Ma, Ziwen Peng, Guorong Wu	In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data.
82	Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation	Tao He, Lianli Gao, Jingkuan Song, Jianfei Cai, Yuan-Fang Li	In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail.
83	Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection	Lianli Gao, Zhilong Zhou, Heng Tao Shen, Jingkuan Song	In this paper, we propose an effective and end-to-end framework, named Bidirectional Additive Net (BAN), for image edge detection.
84	SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor	Jiachen Xu, Jingyu Gong, Jie Zhou, Xin Tan, Yuan Xie, Lizhuang Ma	In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information.
85	Characterizing Similarity of Visual Stimulus from Associated Neuronal Response	Vikram Ravindra, Ananth Grama	In this paper, we present a novel methodological framework for deconvolving the brain’s response to mixed stimuli into its constituent responses to underlying pure factors.
86	Spatiotemporal Super-Resolution with Cross-Task Consistency and Its Semi-supervised Extension	Han-Yi Lin, Pi-Cheng Hsiu, Tei-Wei Kuo, Yen-Yu Lin	We design a two-stream network for spatiotemporal SR in this work.
87	Generating Person Images with Appearance-aware Pose Stylizer	Siyu Huang, Haoyi Xiong, Zhi-Qi Cheng, Qingzhong Wang, Xingran Zhou, Bihan Wen, Jun Huan, Dejing Dou	In this paper, we present a novel end-to-end framework to generate realistic person images based on given person poses and appearances.
88	SBAT: Video Captioning with Sparse Boundary-Aware Transformer	Tao Jin, Siyu Huang, Ming Chen, Yingming Li, Zhongfei Zhang	In this paper, we focus on the problem of applying the transformer structure to video captioning effectively.
89	Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization	Xinjian Huang, Bo Du, Weiwei Liu	In this paper, based on the non-local self-similarity of an image and the different noise strength across each channel, we propose a MultiChannel Weighted Schatten p-Norm Minimization (MCWSNM) model for RGB color image denoising.
90	Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks	Yawen Huang, Feng Zheng, Danyang Wang, Junyu Jiang, Xiaoqian Wang, Ling Shao	In this paper, we explore the possibility of super-resolving and inpainting images to handle multiple degradations and therefore improve their usability.
91	Real-World Automatic Makeup via Identity Preservation Makeup Net	Zhikun Huang, Zhedong Zheng, Chenggang Yan, Hongtao Xie, Yaoqi Sun, Jianzhong Wang, Jiyong Zhang	To address these two challenges, we introduce a new makeup model, called Identity Preservation Makeup Net (IPM-Net), which preserves not only the background but the critical patterns of the original identity.
92	Human Consensus-Oriented Image Captioning	Ziwei Wang, Zi Huang, Yadan Luo	In this work, we explicitly engage human consensus to measure the quality of ground truth captions in advance, and directly encourage the model to learn high quality captions with high priority.
93	Biased Feature Learning for Occlusion Invariant Face Recognition	Changbin Shao, Jing Huo, Lei Qi, Zhen-Hua Feng, Wenbin Li, Chuanqi Dong, Yang Gao	To address the challenges posed by unknown occlusions, we propose a Biased Feature Learning (BFL) framework for occlusion-invariant face recognition.
94	Channel Pruning via Automatic Structure Search	Mingbao Lin, Rongrong Ji, Yuxin Zhang, Baochang Zhang, Yongjian Wu, Yonghong Tian	In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did.
95	GestureDet: Real-time Student Gesture Analysis with Multi-dimensional Attention-based Detector	Rui Zheng, Fei Jiang, Ruimin Shen	Due to limited computational resources in primary and secondary schools, we propose a real-time student behavior detector based on light-weight MobileNetV2-SSD to reduce the dependency of GPUs.
96	DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue	Xiaoze Jiang, Jing Yu, Yajing Sun, Zengchang Qin, Zihao Zhu, Yue Hu, Qi Wu	In this paper, we propose a novel generative decoding architecture to generate high-quality responses, which moves away from decoding the whole encoded semantics towards the design that advocates both transparency and flexibility.
97	Pay Attention to Devils: A Photometric Stereo Network for Better Details	Yakun Ju, Kin-Man Lam, Yang Chen, Lin Qi, Junyu Dong	We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed.
98	E3SN: Efficient End-to-End Siamese Network for Video Object Segmentation	Meng Lan, Yipeng Zhang, Qinning Xu, Lefei Zhang	This paper proposes an efficient end-to-end Siamese network for VOS.
99	TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking	Siyuan Li, Zhi Zhang, Ziyu Liu, Anna Wang, Linglong Qiu, Feng Du	In this paper, we propose an efficient two-stage architecture which makes full use of the complementarity of two subtasks to achieve robust localization and high-quality proposals generation of the target jointly.
100	Learning Task-aware Local Representations for Few-shot Learning	Chuanqi Dong, Wenbin Li, Jing Huo, Zheng Gu, Yang Gao	This paper proposes an Adaptive Task-aware Local Representations Network (ATL-Net) to address this limitation by introducing episodic attention, which can adaptively select the important local patches among the entire task, as the process of human recognition.
101	Bi-level Probabilistic Feature Learning for Deformable Image Registration	Risheng Liu, Zi Li, Yuxi Zhang, Xin Fan, Zhongxuan Luo	In this paper, we propose a novel bi-level optimization model that enables jointly learning deformation maps and features for image registration.
102	G2RL: Geometry-Guided Representation Learning for Facial Action Unit Intensity Estimation	Yingruo Fan, Zhaojiang Lin	To this end, we propose a novel geometry-guided representation learning (G2RL) method for facial AU intensity estimation.
103	Visual Encoding and Decoding of the Human Brain Based on Shared Features	Chao Li, Baolin Liu, Jianguo Wei	In this paper, we assume that only a portion of neural network features is directly related to human brain signals, which we call shared features.
104	Learning to Discretely Compose Reasoning Module Networks for Video Captioning	Ganchao Tan, Daqing Liu, Meng Wang, Zheng-Jun Zha	In this paper, we propose a novel visual reasoning approach for video captioning, named Reasoning Module Networks (RMN), to equip the existing encoder-decoder framework with the above reasoning capacity.
105	Multi-Scale Spatial-Temporal Integration Convolutional Tube for Human Action Recognition	Haoze Wu, Jiawei Liu, Xierong Zhu, Meng Wang, Zheng-Jun Zha	In this work, we propose a novel and efficient Multi-Scale Spatial-Temporal Integration Convolutional Tube (MSTI) aiming at achieving accurate recognition of actions with lower computational cost.
106	Latent Regularized Generative Dual Adversarial Network For Abnormal Detection	Chengwei Chen, Jing Liu, Yuan Xie, Yin Xiao Ban, Chunyun Wu, Yiqing Tao, Haichuan Song	Due to the training instability issues existed in previous generative adversarial networks (GANs) based methods, in this paper we propose a dual auxiliary autoencoder to make a tradeoff between the capability of generator and discriminator, leading to a more stable training process and high-quality image reconstruction.
107	Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning	Longteng Guo, Jing Liu, Xinxin Zhu, Xingjian He, Jie Jiang, Hanqing Lu	In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL).
108	k-SDPP: Fixed-Size Video Summarization via Sequential Determinantal Point Processes	Jiping Zheng, Ganfeng Lu	In this paper, we propose a new model in the DPP lineage named k-SDPP in vein of sequential determinantal point processes but with fixed user specified size k.
109	Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation	Shengyuan Liu, Pei Lv, Yuzhen Zhang, Jie Fu, Junjin Cheng, Wanqing Li, Bing Zhou, Mingliang Xu	This paper proposes a novel Semi-Dynamic Hypergraph Neural Network (SD-HNN) to estimate 3D human pose from a single image.
110	AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation	Gege Zhang, Qinghua Ma, Licheng Jiao, Fang Liu, Qigong Sun	To address this problem, we first extend adversarial learning to this task and propose a novel framework Attention Adversarial Networks (AttAN). With high-order correlations in label sets learned from the adversarial learning, segmentation network can predict labels closer to the real ones and correct noisy results.
111	Position-Aware Recalibration Module: Learning From Feature Semantics and Feature Position	Xu Ma, Song Fu	We present a new method to improve the representational power of the features in Convolutional Neural Networks (CNNs).
112	Transductive Relation-Propagation Network for Few-shot Learning	Yuqing Ma, Shihao Bai, Shan An, Wei Liu, Aishan Liu, Xiantong Zhen, Xianglong Liu	We propose a transductive relation-propagation graph neural network (TRPN) to explicitly model and propagate such relations across support-query pairs.
113	Few-shot Visual Learning with Contextual Memory and Fine-grained Calibration	Yuqing Ma, Wei Liu, Shihao Bai, Qingyu Zhang, Aishan Liu, Weimin Chen, Xianglong Liu	Inspired by the inverted pyramid theory, to address this problem, we propose an inverted pyramid network (IPN) that intimates the human’s coarse-to-fine cognition paradigm.
114	Dynamic Language Binding in Relational Visual Reasoning	Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran	We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering.
115	Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes	Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chee Seng Chan	This paper proposes a novel deep polarized network (DPN) for learning to hash, in which each channel in the network outputs is pushed far away from zero by employing a differentiable bit-wise hinge-like loss which is dubbed as polarization loss.
116	BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation	Zhen-Liang Ni, Gui-Bin Bian, Guan-An Wang, Xiao-Hu Zhou, Zeng-Guang Hou, Xiao-Liang Xie, Zhen Li, Yu-Han Wang	In this paper, we propose a bilinear attention network with adaptive receptive fields to address these two issues.
117	Hierarchical Instance Feature Alignment for 2D Image-Based 3D Shape Retrieval	Heyu Zhou, Weizhi Nie, Wenhui Li, Dan Song, An-An Liu	To overcome these problems, we propose a novel hierarchical instance feature alignment (HIFA) method for this task.
118	Few-shot Human Motion Prediction via Learning Novel Motion Dynamics	Chuanqi Zang, Mingtao Pei, Yu Kong	In our work, we propose a novel approach named Motion Prediction Network (MoPredNet) for few-short human motion prediction.
119	Set and Rebase: Determining the Semantic Graph Connectivity for Unsupervised Cross-Modal Hashing	Weiwei Wang, Yuming Shen, Haofeng Zhang, Yazhou Yao, Li Liu	To address the problem above, in this paper, we propose a novel unsupervised hashing method called Semantic-Rebased Cross-modal Hashing (SRCH).
120	Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings	Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin, Hengshuai Yao, Martin Jagersand	We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embedding.
121	Overflow Aware Quantization: Accelerating Neural Network Inference by Low-bit Multiply-Accumulate Operations	Hongwei Xie, Yafei Song, Ling Cai, Mingyang Li	To address this problem, we propose an overflow aware quantization method by designing trainable adaptive fixed-point representation, to optimize the number of bits for each input tensor while prohibiting numeric overflow during the computation.
122	Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error	Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, Edward McFowland	We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations.
123	Consistent Domain Structure Learning and Domain Alignment for 2D Image-Based 3D Objects Retrieval	Yuting Su, Yuqian Li, Dan Song, Weizhi Nie, Wenhui Li, An-An Liu	In this paper, we propose a domain alignment framework with consistent domain structure learning to reduce the large gap between 2D images and 3D objects.
124	Diagnosing the Environment Bias in Vision-and-Language Navigation	Yubo Zhang, Hao Tan, Mohit Bansal	In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias.
125	Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification	Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Huang Da, Jun Cheng, Bin Hu	Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner.
126	Zero-Shot Object Detection via Learning an Embedding from Semantic Space to Visual Space	Licheng Zhang, Xianzhi Wang, Lina Yao, Lin Wu, Feng Zheng	In this paper, instead, we propose to learn a deep embedding from the semantic space to the visual space, which enables to well alleviate the hubness problem, because, compared with semantic space or joint embedding space, the distribution in visual space has smaller variance.
127	Unsupervised Vehicle Re-identification with Progressive Adaptation	Jinjia Peng, Yang Wang, Huibing Wang, Zhao Zhang, Xianping Fu, Meng Wang	To tackle these challenges, we propose a novel Progressive Adaptation Learning method for vehicle reID, named PAL, which infers from the abundant data without annotations.
128	Recurrent Relational Memory Network for Unsupervised Image Captioning	Dan Guo, Yang Wang, Peipei Song, Meng Wang	In this paper, we propose a novel memory-based network rather than GAN, named Recurrent Relational Memory Network (R2M).
129	Temporal Adaptive Alignment Network for Deep Video Inpainting	Ruixin Liu, Zhenyu Weng, Yuesheng Zhu, Bairong Li	To alleviate above problem, we propose a novel end-to-end Temporal Adaptive Alignment Network(TAAN) for video inpainting.
130	Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation	Pin Jiang, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, Bingshuai Li	In this work, we devise a general bidirectional adversarial training method and employ gradient to guide adversarial examples across the domain gap, i.e., the Adaptive Adversarial Training (AAT) for source to target domain and Entropy-penalized Virtual Adversarial Training (E-VAT) for target to source domain.
131	Hierarchical Attention Based Spatial-Temporal Graph-to-Sequence Learning for Grounded Video Description	Kai Shen, Lingfei Wu, Fangli Xu, Siliang Tang, Jun Xiao, Yueting Zhuang	To address these issues, we cast the GVD task as a spatial-temporal Graph-to-Sequence learning problem, where we model video frames as spatial-temporal sequence graph in order to better capture implicit structural relationships.
132	Polar Relative Positional Encoding for Video-Language Segmentation	Ke Ning, Lingxi Xie, Fei Wu, Qi Tian	In this paper, we tackle a challenging task named video-language segmentation.
133	Label-Attended Hashing for Multi-Label Image Retrieval	Yanzhao Xie, Yu Liu, Yangtao Wang, Lianli Gao, Peng Wang, Ke Zhou	We propose a Label-Attended Hashing (LAH) algorithm which enables an end-to-end hash model with inter-dependency feature extraction.
134	Self-supervised Monocular Depth and Visual Odometry Learning with Scale-consistent Geometric Constraints	Mingkang Xiong, Zhenghong Zhang, Weilin Zhong, Jinsheng Ji, Jiyuan Liu, Huilin Xiong	In this paper, robust geometric losses are proposed to deal with this problem.
135	DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion	Zixiang Zhao, Shuang Xu, Chunxia Zhang, Junmin Liu, Jiangshe Zhang, Pengfei Li	This paper proposes a novel auto-encoder (AE) based fusion network.
136	TRP: Trained Rank Pruning for Efficient Deep Neural Networks	Yuhui Xu, Yuxi Li, Shuai Zhang, Wei Wen, Botao Wang, Yingyong Qi, Yiran Chen, Weiyao Lin, Hongkai Xiong	We propose Trained Rank Pruning (TRP), which alternates between low rank approximation and training.
137	Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval	Xinxun Xu, Muli Yang, Yanhua Yang, Hao Wang	In this paper, we propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR.
138	HAF-SVG: Hierarchical Stochastic Video Generation with Aligned Features	Zhihui Lin, Chun Yuan, Maomao Li	In this paper, we propose a hierarchical recurrent VAE with a feature aligner, which can not only relax the independence assumption in typical VAE but also use a feature aligner to enable the decoder to obtain the aligned spatial information from the last observed frames.
139	Feature Augmented Memory with Global Attention Network for VideoQA	Jiayin Cai, Chun Yuan, Cheng Shi, Lei Li, Yangyang Cheng, Ying Shan	To tackle these problems, we propose a novel VideoQA framework which progressively refines the representations of videos and questions from fine to coarse grain in a sequence-sensitive manner.
140	Exploiting Visual Semantic Reasoning for Video-Text Retrieval	Zerun Feng, Zhimin Zeng, Caili Guo, Zheng Li	To address this issue, we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit reasoning between frame regions.
141	Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos	Jiawei Liu, Zheng-Jun Zha, Xierong Zhu, Na Jiang	In this work, we propose a novel Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person re-identification in videos.
142	Self-Supervised Tuning for Few-Shot Segmentation	Kai Zhu, Wei Zhai, Yang Cao	To address this issue, this paper presents an adaptive tuning framework, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme, augmenting category-specific descriptors for label prediction.
143	A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification	Mengxi Jia, Yunpeng Zhai, Shijian Lu, Siwei Ma, Jian Zhang	This paper presents a novel similarity inference metric (SIM) that exploits the intra-modality sample similarities to circumvent the cross-modality discrepancy targeting optimal cross-modality image matching.
144	CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs	Li’an Zhuo, Baochang Zhang, Hanlin Chen, Linlin Yang, Chen Chen, Yanjun Zhu, David Doermann	To this end, a Child-Parent model is introduced to a differentiable NAS to search the binarized architecture(Child) under the supervision of a full-precision model (Parent).
145	Weakly Supervised Local-Global Relation Network for Facial Expression Recognition	Haifeng Zhang, Wen Su, Jun Yu, Zengfu Wang	To extract crucial local features and enhance the complementary relation between local and global features, this paper proposes a Weakly Supervised Local-Global Relation Network (WS-LGRN), which uses the attention mechanism to deal with part location and feature fusion problems.
146	Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift	Qinming Zhang, Luyan Liu, Kai Ma, Cheng Zhuo, Yefeng Zheng	In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy.
147	Dress like an Internet Celebrity: Fashion Retrieval in Videos	Hongrui Zhao, Jin Yu, Yanan Li, Donghui Wang, Jie Liu, Hongxia Yang, Fei Wu	In this paper, we propose a novel deep neural network, called Detect, Pick, and Retrieval Network (DPRNet), to break the gap between fashion products from videos and audiences.
148	Video Question Answering on Screencast Tutorials	Wentian Zhao, Seokhwan Kim, Ning Xu, Hailin Jin	This paper presents a new video question answering task on screencast tutorials. We introduce a dataset including question, answer and context triples from the tutorial videos for a software.
149	Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding	Zhu Zhang, Zhou Zhao, Zhijie Lin, Baoxing Huai, Jing Yuan	In this paper, we explore spatio-temporal video grounding on unaligned data and multi-form sentences.
150	Unsupervised Scene Adaptation with Memory Regularization in vivo	Zhedong Zheng, Yi Yang	In this paper, we propose an orthogonal method, called memory regularization in vivo, to exploit the intra-domain knowledge and regularize the model training.
151	Overcoming Language Priors with Self-supervised Learning for Visual Question Answering	Xi Zhu, Zhendong Mao, Chunxiao Liu, Peng Zhang, Bin Wang, Yongdong Zhang	In this paper, we introduce a self-supervised learning framework to solve this problem. Concretely, we first automatically generate labeled data to balance the biased data, and then propose a self-supervised auxiliary task to utilize the balanced data to assist the VQA model to overcome language priors.
152	Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition	Yaohui Zhu, Chenlong Liu, Shuqiang Jiang	In this work, we propose a multi-attention meta-learning (MattML) method for few-shot fine-grained image recognition (FSFGIR).
153	Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering	Zihao Zhu, Jing Yu, Yujing Wang, Yajing Sun, Yue Hu, Qi Wu	In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features.
154	Action-Guided Attention Mining and Relation Reasoning Network for Human-Object Interaction Detection	Xue Lin, Qi Zou, Xixia Xu	In this paper, we propose an Action-Guided attention mining and Relation Reasoning (AGRR) network to solve the problems.
155	A Graph-based Interactive Reasoning for Human-Object Interaction Detection	Dongming Yang, Yuexian Zou	In this paper, we present a novel graph-based interactive reasoning model called Interactive Graph (abbr.
156	Diversity of Solutions: An Exploration Through the Lens of Fixed-Parameter Tractability Theory	Julien Baste, Michael R. Fellows, Lars Jaffke, Tomáš Masa?ík, Mateus de Oliveira Oliveira, Geevarghese Philip, Frances A. Rosamond	In this work we initiate a systematic study of diversity from the point of view of fixed-parameter tractability theory.
157	Early and Efficient Identification of Useless Constraint Propagation for Alldifferent Constraints	Xizhe Zhang, Jian Gao, Yizhi Lv, Weixiong Zhang	Here we present a novel theorem for identifying the edges in a value graph of alldifferent constraint whose removal can significantly reduce useless constraint propagation.
158	Subgraph Isomorphism Meets Cutting Planes: Solving With Certified Solutions	Stephan Gocht, Ciaran McCreesh, Jakob Nordström	We show that all of this reasoning can be justified compactly using the cutting planes proofs studied in complexity theory.
159	Extended Conjunctive Normal Form and An Efficient Algorithm for Cardinality Constraints	Zhendong Lei, Shaowei Cai, Chuan Luo	In this paper, we introduce Extended Conjunctive Normal Form (ECNF), which expresses cardinality constraints straightforward and does not need auxiliary variables or clauses.
160	On Irrelevant Literals in Pseudo-Boolean Constraint Learning	Daniel Le Berre, Pierre Marquis, Stefan Mengel, Romain Wallon	In this paper, we show that PB constraints derived using cutting planes may contain irrelevant literals, i.e., literals whose assigned values (whatever they are) never change the truth value of the constraint.
161	Fast and Parallel Decomposition of Constraint Satisfaction Problems	Georg Gottlob, Cem Okulmus, Reinhard Pichler	In this paper, we present a number of key algorithmic improvements and parallelisation techniques to compute so-called Generalized Hypertree Decompositions (GHDs) faster.
162	Learning Sensitivity of RCPSP by Analyzing the Search Process	Marc-André Ménard, Claude-Guy Quimper, Jonathan Gaudreault	We present a new method for sensitivity analysis applied to constraint programming.
163	Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost	Hao Hu, Mohamed Siala, Emmanuel Hebrard, Marie-José Huguet	In this paper we show how the SAT model proposed by [Narodytska et.al 2018] can be lifted to a MaxSAT approach, making it much more practically relevant.
164	NLocalSAT: Boosting Local Search with Solution Prediction	Wenjie Zhang, Zeyu Sun, Qihao Zhu, Ge Li, Shaowei Cai, Yingfei Xiong, Lu Zhang	To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network.
165	Bipartite Encoding: A New Binary Encoding for Solving Non-Binary CSPs	Ruiwei Wang, Roland H.C. Yap	We propose a new binary encoding, called Bipartite Encoding (BE) which uses the idea of partitioning constraints.
166	Automatic Dominance Breaking for a Class of Constraint Optimization Problems	Jimmy Lee, Allen Zhong	We present a theoretical framework for a useful class of constraint optimization problems to detect dominance automatically and formulate the generation of the associated dominance breaking nogoods as constraint satisfaction.
167	Cause-Effect Association between Event Pairs in Event Datasets	Debarun Bhattacharjya, Tian Gao, Nicholas Mattei, Dharmashankar Subramanian	In this paper, we consider datasets involving irregular occurrences of various types of events over the timeline. We propose a suite of scores and related algorithms for estimating the cause-effect association between pairs of events from such large event datasets.
168	Inductive Link Prediction for Nodes Having Only Attribute Information	Yu Hao, Xin Cao, Yixiang Fang, Xike Xie, Sibo Wang	To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism.
169	Optimal Region Search with Submodular Maximization	Xuefeng Chen, Xin Cao, Yifeng Zeng, Yixiang Fang, Bin Yao	In this paper, we study the problem of optimal region search with submodular maximization (ORS-SM).
170	Discrete Embedding for Latent Networks	Hong Yang, Ling Chen, Minglong Lei, Lingfeng Niu, Chuan Zhou, Peng Zhang	To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades.
171	GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension	Yu Chen, Lingfei Wu, Mohammed J. Zaki	In this paper, we first propose a simple yet effective graph structure learning technique to dynamically construct a question and conversation history aware context graph at each conversation turn. Then we propose a novel Recurrent Graph Neural Network, and based on that, we introduce a flow mechanism to model the temporal dependencies in a sequence of context graphs.
172	Motif-Preserving Temporal Network Embedding	Hong Huang, Zixuan Fang, Xiao Wang, Youshan Miao, Hai Jin	Specifically, we propose MTNE, a novel embedding model for temporal networks.
173	Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact	Adam Goodge, Bryan Hooi, See Kiong Ng, Wee Siong Ng	In this work, we study the effect of adversarial attacks on the performance of anomaly-detecting autoencoders using real data from a Cyber physical system (CPS) testbed with intervals of controlled, physical attacks as anomalies.
174	Opinion Maximization in Social Trust Networks	Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu	In this study, we focus on social trust networks (STNs), which have the significant characteristics ignored in the previous studies.
175	MR-GCN: Multi-Relational Graph Convolutional Networks based on Generalized Tensor Product	Zhichao Huang, Xutao Li, Yunming Ye, Michael K. Ng	In this paper, we propose the Multi-Relational Graph Convolutional Network (MR-GCN) framework by developing a novel convolution operator on multi-relational graphs.
176	On the Enumeration of Association Rules: A Decomposition-based Approach	Yacine Izza, Said Jabbour, Badran Raddaoui, Abdelahmid Boudane	This paper introduces a practical SAT-based approach to discover efficiently (minimal non-redundant) association rules.
177	Speeding up Very Fast Decision Tree with Low Computational Cost	Jian Sun, Hongyu Jia, Bo Hu, Xiao Huang, Hao Zhang, Hai Wan, Xibin Zhao	Compared with state-of-the-art algorithms, our method reduces split-attempts by about 5 to 10 times on average with much lower split-delay, which makes our algorithm run faster and more accurate.
178	Simultaneous Arrival Matching for New Spatial Crowdsourcing Platforms	Boyang Li, Yurong Cheng, Ye Yuan, Guoren Wang, Lei Chen	Thus, in this paper, we propose a new Simultaneous Arrival Matching (SAM), which enables workers and users to arrive at their assigned workplace within a given tolerant time.
179	Inductive Anomaly Detection on Attributed Networks	Kaize Ding, Jundong Li, Nitin Agarwal, Huan Liu	In this study, we propose to tackle the problem of inductive anomaly detection on attributed networks with a novel unsupervised framework: Aegis (adversarial graph differentiation networks).
180	Enhancing Urban Flow Maps via Neural ODEs	Fan Zhou, Liang Li, Ting Zhong, Goce Trajcevski, Kunpeng Zhang, Jiahao Wang	We propose to tackle the urban flows inference using dynamic systems paradigm and present a new method FODE — FSR with Ordinary Differential Equations (ODEs).
181	When Do GNNs Work: Understanding and Improving Neighborhood Aggregation	Yiqing Xie, Sha Li, Carl Yang, Raymond Chi-Wing Wong, Jiawei Han	This paper aims to provide a better understanding of this mechanisms by asking the following question: Is neighborhood aggregation always necessary and beneficial?
182	A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data	Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Bei Chen, Xiangjun Dong	Thus, in this paper, we propose a spatial missing data imputation method for multi-view urban statistical data.
183	GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions	Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xuemin Lin	In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way.
184	GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification	Ziyu Jia, Youfang Lin, Jing Wang, Ronghao Zhou, Xiaojun Ning, Yuanlai He, Yaoshuai Zhao	In this paper, we propose a novel deep graph neural network, named GraphSleepNet, for automatic sleep stage classification.
185	A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling	Jie Feng, Ziqian Lin, Tong Xia, Funing Sun, Diansheng Guo, Yong Li	In this paper, we propose Convolution based Sequential and Cross Network (CSCNet) to solve them.
186	Rebalancing Expanding EV Sharing Systems with Deep Reinforcement Learning	Man Luo, Wenzhe Zhang, Tianyou Song, Kun Li, Hongming Zhu, Bowen Du, Hongkai Wen	We propose a novel approach of policy optimization with action cascading, which isolates the non-stationarity locally, and use two connected networks to solve the formulated MARL.
187	Understanding the Success of Graph-based Semi-Supervised Learning using Partially Labelled Stochastic Block Model	Avirup Saha, Shreyas Sheshadri, Samik Datta, Niloy Ganguly, Disha Makhija, Priyank Patel	In this work, we draw upon the notion of recovery from the literature on community detection, and provide guarantees on accuracy for partially-labelled graphs generated from the Partially-Labelled Stochastic Block Model (PLSBM).
188	Multi-Channel Graph Neural Networks	Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, Xia Hu	To compensate the shrinking loss and learn the various nodes’ characteristics, we propose the multi-channel graph neural networks (MuchGNN).
189	Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression	Peiyan Li, Honglian Wang, Christian Böhm, Junming Shao	In this paper, we present a novel Online Semi-supervised Multi-Label learning algorithm (OnSeML) based on label compression and local smooth regression, which allows real-time multi-label predictions in a semi-supervised setting and is robust to evolving label distributions.
190	Network Schema Preserving Heterogeneous Information Network Embedding	Jianan Zhao, Xiao Wang, Chuan Shi, Zekuan Liu, Yanfang Ye	In this paper, we make the first attempt to study network schema preserving HIN embedding, and propose a novel model named NSHE.
191	Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning	Ximing Li, Yang Wang	In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels.
192	JANE: Jointly Adversarial Network Embedding	Liang Yang, Yuexue Wang, Junhua Gu, Chuan Wang, Xiaochun Cao, Yuanfang Guo	To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features.
193	Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification	Lianwei Wu, Yuan Rao, Xiong Yang, Wanzhen Wang, Ambreen Nazir	In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification.
194	Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation	Shuo Zhang, Lei Xie	In this work, we present a theoretical analysis of the representational properties of the GNN that adopts the attention mechanism as an aggregator.
195	Graph Neural Architecture Search	Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu	In this paper, we present a graph neural architecture search method (GraphNAS) that enables automatic design of the best graph neural architecture based on reinforcement learning.
196	Domain Adaptive Classification on Heterogeneous Information Networks	Shuwen Yang, Guojie Song, Yilun Jin, Lun Du	In this paper, we propose Multi-space Domain Adaptive Classification (MuSDAC) to handle the problem of DA on HINs.
197	Rumor Detection on Social Media with Graph Structured Adversarial Learning	Xiaoyu Yang, Yuefei Lyu, Tian Tian, Yifei Liu, Yudong Liu, Xi Zhang	Our focus in this work is to develop a robust graph-based detector to identify rumors on social media from an adversarial perspective.
198	Joint Representation Learning of Legislator and Legislation for Roll Call Prediction	Yuqiao Yang, Xiaoqiang Lin, Geng Lin, Zengfeng Huang, Changjian Jiang, Zhongyu Wei	In this paper, we explore to learn representations of legislation and legislator for the prediction of roll call results.
199	Entity Synonym Discovery via Multipiece Bilateral Context Matching	Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu	To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness.
200	BANANA: when Behavior ANAlysis meets social Network Alignment	Fuxin Ren, Zhongbao Zhang, Jiawei Zhang, Sen Su, Li Sun, Guozhen Zhu, Congying Guo	Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem.
201	Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization	Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh	In this paper, we propose a novel proximal gradient algorithm with momentum and parameter restart for solving nonconvex and nonsmooth problems.
202	Bilinear Graph Neural Network with Neighbor Interactions	Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang	In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN.
203	Unsatisfiability Proofs for Weight 16 Codewords in Lam’s Problem	Curtis Bright, Kevin K.H. Cheung, Brett Stevens, Ilias Kotsireas, Vijay Ganesh	We performed a verification of these searches by reducing the problem to the Boolean satisfiability problem (SAT).
204	Two-goal Local Search and Inference Rules for Minimum Dominating Set	Shaowei Cai, Wenying Hou, Yiyuan Wang, Chuan Luo, Qingwei Lin	This paper develops an efficient local search algorithm for MinDS, which has two main ideas.
205	Self-Guided Evolution Strategies with Historical Estimated Gradients	Fei-Yu Liu, Zi-Niu Li, Chao Qian	In this paper, we propose a new ES algorithm SGES, which utilizes historical estimated gradients to construct a low-dimensional subspace for sampling search directions, and adjusts the importance of this subspace adaptively.
206	Vertex Weighting-Based Tabu Search for p-Center Problem	Qingyun Zhang, Zhipeng Lü, Zhouxing Su, Chumin Li, Yuan Fang, Fuda Ma	In this paper, we transform the p-center problem into a series of set covering subproblems, and propose a vertex weighting-based tabu search (VWTS) algorithm to solve them.
207	Exploration Based Language Learning for Text-Based Games	Andrea Madotto, Mahdi Namazifar, Joost Huizinga, Piero Molino, Adrien Ecoffet, Huaixiu Zheng, Alexandros Papangelis, Dian Yu, Chandra Khatri, Gokhan Tur	In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
208	Intelligent Virtual Machine Provisioning in Cloud Computing	Chuan Luo, Bo Qiao, Xin Chen, Pu Zhao, Randolph Yao, Hongyu Zhang, Wei Wu, Andrew Zhou, Qingwei Lin	In this work, we formulate the practical scenario as the predictive VM provisioning (PreVMP) problem, where upcoming demands are unknown and need to be predicted in advance, and then the VM provisioning plan is optimized based on the predicted demands.
209	NuCDS: An Efficient Local Search Algorithm for Minimum Connected Dominating Set	Bohan Li, Xindi Zhang, Shaowei Cai, Jinkun Lin, Yiyuan Wang, Christian Blum	In this paper, we propose two novel ideas, and develop a new local search algorithm for MCDS called NuCDS.
210	Structured Probabilistic End-to-End Learning from Crowds	Zhijun Chen, Huimin Wang, Hailong Sun, Pengpeng Chen, Tao Han, Xudong Liu, Jie Yang	This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks.
211	LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition	Xiang Cheng, Yunzhe Hao, Jiaming Xu, Bo Xu	Inspired by Lateral Interactions in neuroscience, we propose a high-performance and noise-robust Spiking Neural Network (dubbed LISNN).
212	Learning to Complement Humans	Bryan Wilder, Eric Horvitz, Ece Kamar	We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams by considering the distinct abilities of people and machines.
213	Performance as a Constraint: An Improved Wisdom of Crowds Using Performance Regularization	Jiyi Li, Yasushi Kawase, Yukino Baba, Hisashi Kashima	In this paper, we propose to utilize the worker performance as a global constraint for inferring the true answers.
214	Aggregating Crowd Wisdom with Side Information via a Clustering-based Label-aware Autoencoder	Li’ang Yin, Yunfei Liu, Weinan Zhang, Yong Yu	In this paper, we propose a clustering-based label-aware autoencoder (CLA) to alleviate label noise.
215	Incorporating Failure Events in Agents’ Decision Making to Improve User Satisfaction	David Sarne, Chen Rozenshtein	In particular, we focus on the way an agent’s failures should affect its decision making, as far as user satisfaction measures are concerned.
216	Optimal Complex Task Assignment in Service Crowdsourcing	Feilong Tang	In this paper, we investigate how to realize optimal complex task assignment.
217	Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data	Zhijie Fang, Weiqun Wang, Shixin Ren, Jiaxing Wang, Weiguo Shi, Xu Liang, Chen-Chen Fan, Zeng-Guang Hou	In this paper, we propose a novel regional attention convolutional neural network (RACNN) to take full advantage of spectral-spatial-temporal features for EEG motion intention recognition.
218	Improving Knowledge Tracing via Pre-training Question Embeddings	Yunfei Liu, Yang Yang, Xianyu Chen, Jian Shen, Haifeng Zhang, Yong Yu	In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings.
219	Boolean Games: Inferring Agents’ Goals Using Taxation Queries	Abhijin Adiga, Sarit Kraus, Oleg Maksimov, S. S. Ravi	We present algorithms that show how taxation schemes can also be used to infer agents’ goals.
220	Pitfalls of Learning a Reward Function Online	Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg	We consider a continual (“one life”) learning approach where the agent both learns the reward function and optimises for it at the same time. We show that this comes with a number of pitfalls, such as deliberately manipulating the learning process in one direction, refusing to learn, “learning” facts already known to the agent, and making decisions that are strictly dominated (for all relevant reward functions).
221	A Dataset Complexity Measure for Analogical Transfer	Fadi Badra	To alleviate this problem, this paper presents a dataset complexity measure that can be used either to select an optimal similarity measure, or if the similarity measure is given, to perform analogical transfer: among the potential outcomes of a new situation, the most plausible is the one which minimizes the dataset complexity.
222	Answering Counting Queries over DL-Lite Ontologies	Meghyn Bienvenu, Quentin Manière, Michaël Thomazo	In this paper, we introduce a general form of counting query, relate it to previous proposals, and study the complexity of answering such queries in the presence of DL-Lite ontologies.
223	Implementing Theory of Mind on a Robot Using Dynamic Epistemic Logic	Lasse Dissing, Thomas Bolander	This paper provides evidence for the claims by documenting the implementation of a DEL-based reasoning system on a humanoid robot.
224	Maximizing the Spread of an Opinion in Few Steps: Opinion Diffusion in Non-Binary Networks	Robert Bredereck, Lilian Jacobs, Leon Kellerhals	We consider the setting of asynchronous opinion diffusion with majority threshold: given a social network with each agent assigned to one opinion, an agent will update its opinion if more than half of its neighbors agree on a different opinion.
225	Diagnosing Software Faults Using Multiverse Analysis	Prantik Chatterjee, Abhijit Chatterjee, Jose Campos, Rui Abreu, Subhajit Roy	In this work, we propose a different approach, Multiverse Analysis, that considers multiple hypothetical universes, each corresponding to a scenario where one of the components is assumed to be faulty, to generate a spectrum that attempts to reduce the expected worst-case wasted effort over all the universes.
226	Deductive Module Extraction for Expressive Description Logics	Patrick Koopmann, Jieying Chen	We present methods based on uniform interpolation for extracting different variants of deductive modules, satisfying properties such as completeness, minimality and robustness under replacements, the latter being particularly relevant for ontology reuse.
227	Neural Entity Summarization with Joint Encoding and Weak Supervision	Junyou Li, Gong Cheng, Qingxia Liu, Wen Zhang, Evgeny Kharlamov, Kalpa Gunaratna, Huajun Chen	In this paper, we present a supervised approach NEST that is based on our novel neural model to jointly encode graph structure and text in KGs and generate high-quality diversified summaries.
228	Switch-List Representations in a Knowledge Compilation Map	Ond?ej ?epek, Miloš Chromý	In this paper we focus on a less usual way to represent Boolean functions, namely on representations by switch-lists.
229	Counting Query Answers over a DL-Lite Knowledge Base	Diego Calvanese, Julien Corman, Davide Lanti, Simon Razniewski	In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration.
230	Deep Learning for Abstract Argumentation Semantics	Dennis Craandijk, Floris Bex	In this paper, we present a learning-based approach to determining acceptance of arguments under several abstract argumentation semantics.
231	Synthesizing strategies under expected and exceptional environment behaviors	Benjamin Aminof, Giuseppe De Giacomo, Alessio Lomuscio, Aniello Murano, Sasha Rubin	We formalize these concepts in the context of linear-temporal logic, and give an algorithm for solving this problem.
232	A Framework for Reasoning about Dynamic Axioms in Description Logics	Bartosz Bednarczyk, Stephane Demri, Alessio Mansutti	In the paper, we focus on ALC and EL augmented with dynamic axioms, or to their subclass of positive dynamic axioms.
233	Overcoming the Grounding Bottleneck Due to Constraints in ASP Solving: Constraints Become Propagators	Bernardo Cuteri, Carmine Dodaro, Francesco Ricca, Peter Schüller	In this paper, we develop an innovative approach for evaluating ASP programs, where some of the constraints of the input program are not grounded but automatically translated into propagators of the CDCL algorithm that work on partial interpretations.
234	A Logic of Directions	Heshan Du, Natasha Alechina, Anthony G. Cohn	We propose a logic of directions for points (LD) over 2D Euclidean space, which formalises primary direction relations east (E), west (W), and indeterminate east/west (Iew), north (N), south (S) and indeterminate north/south (Ins).
235	Automatic Synthesis of Generalized Winning Strategies of Impartial Combinatorial Games Using SMT Solvers	Kaisheng Wu, Liangda Fang, Liping Xiong, Zhao-Rong Lai, Yong Qiao, Kaidong Chen, Fei Rong	In this paper, we investigate synthesizing generalized winning strategies for ICGs.
236	Revisiting the Notion of Extension over Incomplete Abstract Argumentation Frameworks	Bettina Fazzinga, Sergio Flesca, Filippo Furfaro	Thus, we introduce the alternative notion of i*-extension overcoming the highlighted problems, and provide a thorough complexity characterization of the corresponding verification problem.
237	All-Instances Oblivious Chase Termination is Undecidable for Single-Head Binary TGDs	Bartosz Bednarczyk, Robert Ferens, Piotr Ostropolski-Nalewaja	In this work, we show that undecidability occurs already for sets of single-head TGD over binary vocabularies.
238	Semantic Width and the Fixed-Parameter Tractability of Constraint Satisfaction Problems	Hubie Chen, Georg Gottlob, Matthias Lanzinger, Reinhard Pichler	The presented work resolves a long-standing open problem and yields powerful new tools for complexity research in AI and database theory.
239	Smart Voting	Rachael Colley, Umberto Grandi, Arianna Novaro	We propose a generalisation of liquid democracy in which a voter can either vote directly on the issues at stake, delegate her vote to another voter, or express complex delegations to a set of trusted voters.
240	The Complexity Landscape of Resource-Constrained Scheduling	Robert Ganian, Thekla Hamm, Guillaume Mescoff	In the first part of our paper, we develop new algorithms and give hardness-proofs in order to obtain a detailed complexity map of (M)RCPSP that settles the complexity of all 1024 considered variants of the problem defined in terms of explicit restrictions of natural parameters of instances.
241	Enriching Documents with Compact, Representative, Relevant Knowledge Graphs	Shuxin Li, Zixian Huang, Gong Cheng, Evgeny Kharlamov, Kalpa Gunaratna	To find compact, representative, and relevant ERGs for effective enrichment, we propose an efficient best-first search algorithm to solve a new combinatorial optimization problem that achieves a trade-off between representativeness and compactness, and then we exploit ontological knowledge to rank ERGs by entity-based document-KG and intra-KG relevance.
242	NeurASP: Embracing Neural Networks into Answer Set Programming	Zhun Yang, Adam Ishay, Joohyung Lee	We present NeurASP, a simple extension of answer set programs by embracing neural networks.
243	Belief Merging Operators as Maximum Likelihood Estimators	Patricia Everaere, Sebastien Konieczny, Pierre Marquis	In this paper, some logical connections between the rationality postulates for belief merging (IC postulates) and simple conditions over the noise model under consideration are exhibited.
244	On Computational Aspects of Iterated Belief Change	Nicolas Schwind, Sebastien Konieczny, Jean-Marie Lagniez, Pierre Marquis	This paper focuses on the inference problem for iterated belief change, when belief states are represented as a special kind of stratified belief bases.
245	Rewriting the Description Logic ALCHIQ to Disjunctive Existential Rules	David Carral, Markus Krötzsch	We therefore develop small rewritings for the DL \ALCHIQ — featuring disjunction, number restrictions, and inverse roles — to disjunctive Datalog.
246	Lower Bounds and Faster Algorithms for Equality Constraints	Peter Jonsson, Victor Lagerkvist	We study the fine-grained complexity of NP-complete, infinite-domain constraint satisfaction problems (CSPs) parameterised by a set of first-order definable relations (with equality).
247	Controlled Query Evaluation in Description Logics Through Instance Indistinguishability	Gianluca Cima, Domenico Lembo, Riccardo Rosati, Domenico Fabio Savo	Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability.
248	On the Decidability of Intuitionistic Tense Logic without Disjunction	Fei Liang, Zhe Lin	In this paper, we consider the variety ISt of the expansions of implicative semi-lattices with tense modal operators, which are algebraic models of the disjunction-free fragment of intuitionistic tense logic.
249	A Modal Logic for Joint Abilities under Strategy Commitments	Zhaoshuai Liu, Liping Xiong, Yongmei Liu, Yves Lespérance, Ronghai Xu, Hongyi Shi	In this paper, we propose JAADL, a modal logic for joint abilities under strategy commitments, which is an extension of ATL.
250	On Robustness in Qualitative Constraint Networks	Michael Sioutis, Zhiguo Long, Tomi Janhunen	We introduce and study a notion of robustness in Qualitative Constraint Networks (QCNs), which are typically used to represent and reason about abstract spatial and temporal information.
251	Cone Semantics for Logics with Negation	Özgür Lütfü Özçep, Mena Leemhuis, Diedrich Wolter	This paper presents an embedding of ontologies expressed in the ALC description logic into a real-valued vector space, comprising restricted existential and universal quantifiers, as well as concept negation and concept disjunction.
252	A Journey into Ontology Approximation: From Non-Horn to Horn	Anneke Haga, Carsten Lutz, Johannes Marti, Frank Wolter	We study complete approximations of an ontology formulated in a non-Horn description logic (DL) such as ALC in a Horn DL such as EL.
253	Lower Bounds for Approximate Knowledge Compilation	Alexis de Colnet, Stefan Mengel	In this paper we formalize two notions of approximation: weak approximation which has been studied before in the decision diagram literature and strong approximation which has been used in recent algorithmic results.
254	A Fully Rational Account of Structured Argumentation Under Resource Bounds	Marcello D’Agostino, Sanjay Modgil	In this paper we present a new version of ASPIC+ – Dialectial ASPIC+ – that is fully rational under resource bounds.
255	Solving Analogies on Words based on Minimal Complexity Transformation	Pierre-Alexandre Murena, Marie Al-Ghossein, Jean-Louis Dessalles, Antoine Cornuéjols	In this paper, we propose an alternative approach, based on the assumption that optimal word inflections are transformations of minimal complexity.
256	Controllability of Control Argumentation Frameworks	Andreas Niskanen, Daniel Neugebauer, Matti Järvisalo	We establish a complete computational complexity map of the central computational problem of controllability in CAFs for five key semantics.
257	Provenance for the Description Logic ELHr	Camille Bourgaux, Ana Ozaki, Rafael Penaloza, Livia Predoiu	We address the problem of handling provenance information in ELHr ontologies.
258	On the Learnability of Possibilistic Theories	Cosimo Persia, Ana Ozaki	We investigate learnability of possibilistic theories from entailments in light of Angluin’s exact learning model.
259	Concurrent Games in Dynamic Epistemic Logic	Bastien Maubert, Sophie Pinchinat, Francois Schwarzentruber, Silvia Stranieri	In this work we define a concurrent DEL product, propose a mechanism to resolve conflicts between actions, and define concurrent DEL games.
260	Model-Based Synthesis of Incremental and Correct Estimators for Discrete Event Systems	Stéphanie Roussel, Xavier Pucel, Valentin Bouziat, Louise Travé-Massuyès	In this paper, we show that single-state trackability can be expressed in terms of the simulation relation between automata.
261	Inconsistency Measurement for Improving Logical Formula Clustering	Yakoub Salhi	This study proposes an approach for defining clustering methods that deal with bases of propositional formulas in classical logic, i.e., methods for dividing formula bases into meaningful groups.
262	Threshold Treewidth and Hypertree Width	Robert Ganian, Andre Schidler, Manuel Sorge, Stefan Szeider	Here we introduce an enhancement of tree and hypertree width through a novel notion of thresholds, allowing the associated decompositions to take into account information about the computational costs associated with solving the given CSP instance.
263	Adversarial Oracular Seq2seq Learning for Sequential Recommendation	Pengyu Zhao, Tianxiao Shui, Yuanxing Zhang, Kecheng Xiao, Kaigui Bian	In this paper, we introduce the Adversarial Oracular Seq2seq learning for sequential Recommendation (AOS4Rec), which formulates the sequential recommendation as a seq2seq learning problem to portray time-varying interactions in the recommendation, and exploits the oracular learning and adversarial learning to enhance the recommendation quality.
264	Ranking Semantics for Argumentation Systems With Necessities	Dragan Doder, Srdjan Vesic, Madalina Croitoru	To this end, we (1) provide a set of postulates specifically designed for necessities and (2) propose the first ranking-based semantics in the literature to be shown to respect these postulates.
265	Tractable Fragments of Datalog with Metric Temporal Operators	Przemys?aw A. Wa??ga, Bernardo Cuenca Grau, Mark Kaminski, Egor V. Kostylev	Intractability prompts us to also limit the kinds of temporal operators allowed in rules, and we propose a practical core fragment for which reasoning becomes TC0-complete.
266	Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning	Guojia Wan, Shirui Pan, Chen Gong, Chuan Zhou, Gholamreza Haffari	In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically.
267	Query Answering for Existential Rules via Efficient Datalog Rewriting	Zhe Wang, Peng Xiao, Kewen Wang, Zhiqiang Zhuang, Hai Wan	In this paper, we fill the gap by proposing an efficient datalog rewriting approach for answering conjunctive queries over existential rules, and identify and combine existing fragments of existential rules for which our rewriting method terminates.
268	Model-theoretic Characterizations of Existential Rule Languages	Heng Zhang, Yan Zhang, Guifei Jiang	Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs.
269	Learning and Solving Regular Decision Processes	Eden Abadi, Ronen I. Brafman	Our approach rests on a new representation for RDPs using Mealy Machines that emit a distribution and an expected reward for each state-action pair. Building on this representation, we combine automata learning techniques with history clustering to learn such a Mealy machine and solve it by adapting MCTS to it.
270	Stabilizing Adversarial Invariance Induction from Divergence Minimization Perspective	Yusuke Iwasawa, Kei Akuzawa, Yutaka Matsuo	This paper presents an analysis of the reasons for the optimization difficulties and provides a better optimization procedure by rethinking AII from a divergence minimization perspective.
271	Learning With Subquadratic Regularization : A Primal-Dual Approach	Raman Sankaran, Francis Bach, Chiranjib Bhattacharyya	In this paper, we study the computational aspects of learning with subquadratic norms in a general setup.
272	Learning Interpretable Representations with Informative Entanglements	Ege Beyaz?t, Doruk Tuncel, Xu Yuan, Nian-Feng Tzeng, Xindong Wu	In this paper, we propose a new method to enable Generative Adversarial Networks (GANs) to discover salient features that may be entangled in an informative manner, instead of extracting only disentangled features.
273	Order-Dependent Event Models for Agent Interactions	Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian	We propose an algorithm to discover these causal events and learn the most influential orders using time-stamped event occurrence data.
274	Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals	Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag	The paper presents a machine learning-based approach for the automatic identification of hierarchical MCDM models, composed of 2-additive Choquet integral aggregators and of marginal utility functions on the raw features from data reflecting expert preferences.
275	Reinforcement Learning with Dynamic Boltzmann Softmax Updates	Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang	In this paper, we propose to update the value function with dynamic Boltzmann softmax (DBS) operator, which has good convergence property in the setting of planning and learning.
276	A New Attention Mechanism to Classify Multivariate Time Series	Yifan Hao, Huiping Cao	To address these aforementioned issues, we propose a novel Cross Attention Stabilized Fully Convolutional Neural Network (CA-SFCN) to classify MTS data.
277	Explainable Inference on Sequential Data via Memory-Tracking	Biagio La Rosa, Roberto Capobianco, Daniele Nardi	In this paper we present a novel mechanism to get explanations that allow to better understand network predictions when dealing with sequential data.
278	Positive Unlabeled Learning with Class-prior Approximation	Shizhen Chang, Bo Du, Liangpei Zhang	In this paper, we formulate a convex formulation to jointly solve the class-prior unknown problem and train an accurate classifier with no need of any class-prior assumptions or additional negative samples.
279	IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL	Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam	We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability.
280	Switching Poisson Gamma Dynamical Systems	Wenchao Chen, Bo Chen, Yicheng Liu, Qianru Zhao, Mingyuan Zhou	We propose Switching Poisson gamma dynamical systems (SPGDS) to model sequentially observed multivariate count data.
281	Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge	Gehui Shen, Xi Chen, Zhihong Deng	In this paper, we aim to combine the advantages of above two methods by distilling MCMC samples into an approximate variational posterior.
282	Bayesian Decision Process for Budget-efficient Crowdsourced Clustering	Xiaozhou Wang, Xi Chen, Qihang Lin, Weidong Liu	We propose an optimistic knowledge gradient policy where the assignment of items in each stage is based on the minimum-weight K-cut defined on a similarity graph.
283	An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles	Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani	We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation.
284	Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation	Ziye Chen, Mingming Gong, Lingjuan Ge, Bo Du	In this paper, we apply self-attention (SA) mechanism to boost the performance of deep metric learning.
285	Flow-based Intrinsic Curiosity Module	Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong, Chun-Yi Lee	In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses.
286	Learning Large Logic Programs By Going Beyond Entailment	Andrew Cropper, Sebastijan Duman?ic	We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs.
287	Fully Nested Neural Network for Adaptive Compression and Quantization	Yufei Cui, Ziquan Liu, Wuguannan Yao, Qiao Li, Antoni B. Chan, Tei-wei Kuo, Chun Jason Xue	In this work, we propose a fully nested neural network (FN3) that runs only once to build a nested set of compressed/quantized models, which is optimal for different resource constraints.
288	SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search	Yaoming Wang, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong	In this paper, we propose a novel probabilistic approach, namely Semi-Implicit Variational Dropout one-shot Neural Architecture Search (SI-VDNAS), that leverages semi-implicit variational dropout to support architecture search with variable operations and edges.
289	Potential Driven Reinforcement Learning for Hard Exploration Tasks	Enmin Zhao, Shihong Deng, Yifan Zang, Yongxin Kang, Kai Li, Junliang Xing	Inspired by the potential energy in physics, this work introduces the artificial potential field into experience replay and develops Potentialized Experience Replay (PotER) as a new and effective sampling algorithm for RL in hard exploration tasks with sparse rewards.
290	The Sparse MinMax k-Means Algorithm for High-Dimensional Clustering	Sayak Dey, Swagatam Das, Rammohan Mallipeddi	We propose a Sparse MinMax k-Means Clustering approach by reformulating the objective of the MinMax k-Means algorithm (a variation of classical k-Means that minimizes the maximum intra-cluster variance instead of the sum of intra-cluster variances), into a new weighted between-cluster sum of squares (BCSS) form.
291	Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks	Tuan Hoang, Thanh-Toan Do, Tam V. Nguyen, Ngai-Man Cheung	This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations.
292	Marthe: Scheduling the Learning Rate Via Online Hypergradients	Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi	Based on this, we introduce MARTHE, a novel online algorithm guided by cheap approximations of the hypergradient that uses past information from the optimization trajectory to simulate future behaviour.
293	Coloring Graph Neural Networks for Node Disambiguation	George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux	In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs).
294	Self-paced Consensus Clustering with Bipartite Graph	Peng Zhou, Liang Du, Xuejun Li	To tackle this problem, we propose a novel self-paced consensus clustering method to gradually involve instances from more reliable to less reliable ones into the ensemble learning.
295	Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks	Maxime Wabartha, Audrey Durand, Vincent François-Lavet, Joelle Pineau	To avoid this pitfall, we introduce DENN, an ensemble approach building a set of Diversely Extrapolated Neural Networks that fits the training data and is able to generalize more diversely when extrapolating to novel data points.
296	Non-monotone DR-submodular Maximization over General Convex Sets	Christoph Dürr, Nguyen Kim Thang, Abhinav Srivastav, Léo Tible	In this paper, we show that for maximizing non-monotone DR-submodular functions over a general convex set (such as up-closed convex sets, conic convex set, etc) the Frank-Wolfe algorithm achieves an approximation guarantee which depends on the convex set.
297	Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data	Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Marc Sebban	Learning from imbalanced data, where the positive examples are very scarce, remains a challenging task from both a theoretical and algorithmic perspective. In this paper, we address this problem using a metric learning strategy.
298	Metric Learning in Optimal Transport for Domain Adaptation	Tanguy Kerdoncuff, Rémi Emonet, Marc Sebban	In this paper, we propose to use Optimal Transport (OT) and its associated Wassertein distance to perform this alignment.
299	Memory Augmented Neural Model for Incremental Session-based Recommendation	Fei Mi, Boi Faltings	More importantly, we propose a general framework called Memory Augmented Neural model (MAN).
300	Decorrelated Clustering with Data Selection Bias	Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang	In this paper, we propose a novel Decorrelation regularized K-Means algorithm (DCKM) for clustering with data selection bias.
301	Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!	Dingqi Yang, Benjamin Fankhauser, Paolo Rosso, Philippe Cudre-Mauroux	Against this background, we propose Flashback, a general RNN architecture designed for modeling sparse user mobility traces by doing flashbacks on hidden states in RNNs.
302	Knowledge Hypergraphs: Prediction Beyond Binary Relations	Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole	In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on any number of entities.
303	Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning	Dieqiao Feng, Carla Gomes, Bart Selman	Our approach based on deep reinforcement learning augmented with a curriculum-driven method is the first one to solve hard instances within one day of training while other modern solvers cannot solve these instances within any reasonable time limit.
304	Can Cross Entropy Loss Be Robust to Label Noise?	Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, Bo An	In this paper, we propose a general framework dubbed Taylor cross entropy loss to train deep models in the presence of label noise.
305	Learning Interpretable Models in the Property Specification Language	Rajarshi Roy, Dana Fisman, Daniel Neider	We address the problem of learning human-interpretable descriptions of a complex system from a finite set of positive and negative examples of its behavior.
306	Best Arm Identification in Spectral Bandits	Tomáš Kocák, Aurélien Garivier	Building on this algorithm, we propose an asymptotically optimal strategy.
307	Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering	Tao Shen, Xiubo Geng, Guodong Long, Jing Jiang, Chengqi Zhang, Daxin Jiang	In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions.
308	Human-Driven FOL Explanations of Deep Learning	Gabriele Ciravegna, Francesco Giannini, Marco Gori, Marco Maggini, Stefano Melacci	In this paper, we focus on the case of multi-label classification, proposing a neural network that learns the relationships among the predictors associated to each class, yielding First-Order Logic (FOL)-based descriptions.
309	Disentangling Direct and Indirect Interactions in Polytomous Item Response Theory Models	Frank Nussbaum, Joachim Giesen	Here, we investigate fused latent and graphical models that exhibit continuous latent variables and discrete observed variables.
310	Online Positive and Unlabeled Learning	Chuang Zhang, Chen Gong, Tengfei Liu, Xun Lu, Weiqiang Wang, Jian Yang	Therefore, this paper proposes a novel positive and unlabeled learning algorithm in an online training mode, which trains a classifier solely on the positive and unlabeled data arriving in a sequential order.
311	Batch Decorrelation for Active Metric Learning	Priyadarshini Kumari, Ritesh Goru, Siddhartha Chaudhuri, Subhasis Chaudhuri	In this work, we propose a novel method to decorrelate batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion.
312	Fairness-Aware Neural Rényi Minimization for Continuous Features	Vincent Grari, Sylvain Lamprier, Marcin Detyniecki	The objective in this paper is to ensure some independence level between the outputs of regression models and any given continuous sensitive variables.
313	Towards Accurate and Robust Domain Adaptation under Noisy Environments	Zhongyi Han, Xian-Jin Gui, Chaoran Cui, Yilong Yin	In this paper, we report our attempt towards achieving accurate noise-robust domain adaptation.
314	FNNC: Achieving Fairness through Neural Networks	Manisha Padala, Sujit Gujar	We propose a neural network-based framework, \emph{FNNC}, to achieve fairness while maintaining high accuracy in classification.
315	Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation	Julian Berk, Sunil Gupta, Santu Rana, Svetha Venkatesh	In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function.
316	KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge	Peng Zhang, Jianye Hao, Weixun Wang, Hongyao Tang, Yi Ma, Yihai Duan, Yan Zheng	Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning.
317	Soft Threshold Ternary Networks	Weixiang Xu, Xiangyu He, Tianli Zhao, Qinghao Hu, Peisong Wang, Jian Cheng	In this paper, we present the Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine quantization intervals instead of depending on a hard threshold.
318	DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction	Alexander Schulz, Fabian Hinder, Barbara Hammer	In this paper, we propose to visualize a part of the decision function of a deep neural network together with a part of the data set in two dimensions with discriminative dimensionality reduction.
319	Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning	Céline Hocquette, Stephen H. Muggleton	We consider augmenting top-down MIL systems with a bottom-up step during which the background knowledge is generalised with an extension of the immediate consequence operator for second-order logic programs.
320	Interpretable Models for Understanding Immersive Simulations	Nicholas Hoernle, Kobi Gal, Barbara Grosz, Leilah Lyons, Ada Ren, Andee Rubin	This paper describes methods for comparative evaluation of the interpretability of models of high dimensional time series data inferred by unsupervised machine learning algorithms.
321	DropNAS: Grouped Operation Dropout for Differentiable Architecture Search	Weijun Hong, Guilin Li, Weinan Zhang, Ruiming Tang, Yunhe Wang, Zhenguo Li, Yong Yu	In this paper, we systematically study these problems and propose a novel grouped operation dropout algorithm named DropNAS to fix the problems with DARTS.
322	Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning	Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Longbing Cao	Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model.
323	Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer	Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi, Jie Ma, Jian Zhang	In this work, we propose a framework called Stochastic Batch Augmentation (SBA) to address these problems.
324	Unsupervised Monocular Visual-inertial Odometry Network	Peng Wei, Guoliang Hua, Weibo Huang, Fanyang Meng, Hong Liu	By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper.
325	LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks	Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, Weiyang Kong	In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block.
326	Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning	Hao Zhu, Huaibo Huang, Yi Li, Aihua Zheng, Ran He	In this paper, we propose a novel arbitrary talking face generation framework by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE).
327	Tensor-based multi-view label enhancement for multi-label learning	Fangwen Zhang, Xiuyi Jia, Weiwei Li	In view of the fact that there are many multi-view data in real-world applications, which can provide richer semantic information from different perspectives, this paper first presents a multi-view label enhancement problem and proposes a tensor-based multi-view label enhancement method, named TMV-LE.
328	Privileged label enhancement with multi-label learning	Wenfang Zhu, Xiuyi Jia, Weiwei Li	In this paper, we propose a novel label enhancement method by using privileged information.
329	Optimal Margin Distribution Machine for Multi-Instance Learning	Teng Zhang, Hai Jin	Inspired by this observation, we propose the multi-instance optimal margin distribution machine, which can identify the key instances via explicitly optimizing the margin distribution.
330	Knowledge-Based Regularization in Generative Modeling	Naoya Takeishi, Yoshinobu Kawahara	In this paper, we propose a method to incorporate prior knowledge of feature relations into the learning of general-purpose generative models.
331	Generalized Mean Estimation in Monte-Carlo Tree Search	Tuan Dam, Pascal Klink, Carlo D’Eramo, Jan Peters, Joni Pajarinen	Instead of settling for one of these value estimates, we go a step further proposing a novel backup strategy which uses the power mean operator, which computes a value between the average and maximum value.
332	Logic Constrained Pointer Networks for Interpretable Textual Similarity	Subhadeep Maji, Rohan Kumar, Manish Bansal, Kalyani Roy, Pawan Goyal	In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented using BERT.
333	Learning with Labeled and Unlabeled Multi-Step Transition Data for Recovering Markov Chain from Incomplete Transition Data	Masahiro Kohjima, Takeshi Kurashima, Hiroyuki Toda	In this paper, we propose a new method that can estimate, in a unified manner, Markov chain parameters from incomplete transition data that consist of hidden transition data (data from which visited state information is partially hidden) and dropped transition data (data from which some state visits are dropped).
334	Towards a Hierarchical Bayesian Model of Multi-View Anomaly Detection	Zhen Wang, Chao Lan	In this paper, we propose a novel inductive semi-supervised Bayesian multi-view anomaly detector.
335	Synthesizing Aspect-Driven Recommendation Explanations from Reviews	Trung-Hoang Le, Hady W. Lauw	For more flexible, literate, and varied explanations that cover various aspects of interest, we propose to synthesize an explanation by selecting snippets from reviews to optimize representativeness and coherence.
336	Convolutional Neural Networks with Compression Complexity Pooling for Out-of-Distribution Image Detection	Sehun Yu, Dongha Lee, Hwanjo Yu	In this work, we propose a novel out-of-distribution detection method termed as MALCOM, which neither uses any out-of-distribution sample nor retrains the model.
337	Embodied Multimodal Multitask Learning	Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, Dhruv Batra	We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations.
338	Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems	Huiyuan Chen, Jing Li	To fill this gap, we propose a novel nonlinear tensor machine, which combines deep neural networks and tensor algebra to capture nonlinear interactions among multi-aspect factors.
339	Scalable Gaussian Process Regression Networks	Shibo Li, Wei Xing, Robert M. Kirby, Shandian Zhe	To overcome these limitations, we propose a scalable variational inference algorithm for GPRN, which not only captures the abundant posterior dependencies but also is much more efficient for massive outputs.
340	AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search	Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei Lin, Jingren Zhou	Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.
341	Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling	Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, Scott A. Sisson	In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework — the Recurrent Dirichlet Belief Network~(Recurrent-DBN) — to study interpretable hidden structures from dynamic relational data.
342	Collaboration Based Multi-Label Propagation for Fraud Detection	Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui, Weiwei Liu, Tianlei Hu, Gang Chen	To remedy these problems, this work proposes a collaboration based multi-label propagation (CMLP) algorithm.
343	Contextualized Point-of-Interest Recommendation	Peng Han, Zhongxiao Li, Yong Liu, Peilin Zhao, Jing Li, Hao Wang, Shuo Shang	In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information.
344	MaCAR: Urban Traffic Light Control via Active Multi-agent Communication and Action Rectification	Zhengxu Yu, Shuxian Liang, Long Wei, Zhongming Jin, Jianqiang Huang, Deng Cai, Xiaofei He, Xian-Sheng Hua	In this work, we propose a novel Multi-agent Communication and Action Rectification (MaCAR) framework.
345	Hypothesis Sketching for Online Kernel Selection in Continuous Kernel Space	Xiao Zhang, Shizhong Liao	To address these issues, we propose a novel hypothesis sketching approach to online kernel selection in continuous kernel space, which has constant computational complexities at each round and enjoys a sublinear regret bound.
346	Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning	Ishan Durugkar, Elad Liebman, Peter Stone	We consider a framework for this setting which balances individual preferences against task rewards using a linear mixing scheme.
347	Multi-label Feature Selection via Global Relevance and Redundancy Optimization	Jia Zhang, Yidong Lin, Min Jiang, Shaozi Li, Yong Tang, Kay Chen Tan	In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection.
348	Deep Feedback Network for Recommendation	Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, Leyu Lin	In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation.
349	Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation	Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu	To mitigate this problem, we consider a novel problem setting where the classifier for the target domain has to be trained with complementary-label data from the source domain and unlabeled data from the target domain named budget-friendly UDA (BFUDA).
350	Argot: Generating Adversarial Readable Chinese Texts	Zihan Zhang, Mingxuan Liu, Chao Zhang, Yiming Zhang, Zhou Li, Qi Li, Haixin Duan, Donghong Sun	In this paper, we analyze the differences between Chinese and English, and explore the methodology to transform the existing English adversarial generation method to Chinese.
351	Joint Partial Optimal Transport for Open Set Domain Adaptation	Renjun Xu, Pelen Liu, Yin Zhang, Fang Cai, Jindong Wang, Shuoying Liang, Heting Ying, Jianwei Yin	To overcome this, we propose Joint Partial Optimal Transport (JPOT), fully utilizing information of not only the labeled source domain but also the discriminative representation of unknown class in the target domain.
352	Multivariate Probability Calibration with Isotonic Bernstein Polynomials	Yongqiao Wang, Xudong Liu	To this end, we propose a multivariate regression method based on shape-restricted Bernstein polynomials.
353	Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints	Huanle Xu, Yang Liu, Wing Cheong Lau, Rui Li	In this paper, we study a combinatorial MAB problem with concave objective and fairness constraints.
354	Learning Personalized Itemset Mapping for Cross-Domain Recommendation	Yinan Zhang, Yong Liu, Peng Han, Chunyan Miao, Lizhen Cui, Baoli Li, Haihong Tang	In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN).
355	Label Distribution for Learning with Noisy Labels	Yun-Peng Liu, Ning Xu, Yu Zhang, Xin Geng	To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE).
356	Intent Preference Decoupling for User Representation on Online Recommender System	Zhaoyang Liu, Haokun Chen, Fei Sun, Xu Xie, Jinyang Gao, Bolin Ding, Yanyan Shen	In this paper, we propose a novel learning strategy named FLIP to decouple the learning of intent and preference under the online settings.
357	Beyond Network Pruning: a Joint Search-and-Training Approach	Xiaotong Lu, Han Huang, Weisheng Dong, Xin Li, Guangming Shi	By treating pruning as a search strategy, we present two new insights in this paper: 1) it is possible to expand the search space of networking pruning by associating each filter with a learnable weight; 2) joint search-and-training can be conducted iteratively to maximize the learning efficiency.
358	Collaborative Self-Attention Network for Session-based Recommendation	Anjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, Victor S. Sheng	To this end, we propose a novel solution, Collaborative Self-Attention Network (CoSAN) for session-based recommendation, to learn the session representation and predict the intent of the current session by investigating neighborhood sessions.
359	Sinkhorn Regression	Lei Luo, Jian Pei, Heng Huang	This paper introduces a novel Robust Regression (RR) model, named Sinkhorn regression, which imposes Sinkhorn distances on both loss function and regularization.
360	A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery	Yijing Luo, Bo Han, Chen Gong	To address this issue, this paper introduces a bi-level learning paradigm termed “Spectral Cluster Discovery” (SCD) for combating with noisy labels.
361	Partial Multi-Label Learning via Multi-Subspace Representation	Ziwei Li, Gengyu Lyu, Songhe Feng	To tackle the problem, we propose a novel framework named partial multi-label learning via MUlti-SubspacE Representation (MUSER), where the redundant labels together with noisy features are jointly taken into consideration during the training process.
362	Feature Statistics Guided Efficient Filter Pruning	Hang Li, Chen Ma, Wei Xu, Xue Liu	In this paper, we propose a novel filter pruning method, which incorporates two kinds of feature map selections: diversity-aware selection (DFS) and similarity-aware selection (SFS).
363	On Deep Unsupervised Active Learning	Changsheng Li, Handong Ma, Zhao Kang, Ye Yuan, Xiao-Yu Zhang, Guoren Wang	In this paper, we present a novel Deep neural network framework for Unsupervised Active Learning, called DUAL.
364	Mixed-Variable Bayesian Optimization	Erik Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause	In this paper, we introduce MiVaBo, a novel BO algorithm for the efficient optimization of mixed-variable functions combining a linear surrogate model based on expressive feature representations with Thompson sampling.
365	The Importance of the Test Set Size in Quantification Assessment	André Maletzke, Waqar Hassan, Denis dos Reis, Gustavo Batista	(i) We empirically demonstrate the importance of the test set size to assess quantifiers. (ii) We show that current quantifiers generally have a mediocre performance on the smallest test sets. (iii) We propose a metalearning scheme to select the best quantifier based on the test size that can outperform the best single quantification method.
366	Understanding the Power and Limitations of Teaching with Imperfect Knowledge	Rati Devidze, Farnam Mansouri, Luis Haug, Yuxin Chen, Adish Singla	Inspired by real-world applications of machine teaching in education, we consider the setting where teacher’s knowledge is limited and noisy, and the key research question we study is the following: When does a teacher succeed or fail in effectively teaching a learner using its imperfect knowledge?
367	Self-Attentional Credit Assignment for Transfer in Reinforcement Learning	Johan Ferret, Raphael Marinier, Matthieu Geist, Olivier Pietquin	In this paper, we take a brand-new perspective about transfer: we suggest that the ability to assign credit unveils structural invariants in the tasks that can be transferred to make RL more sample-efficient.
368	Textual Membership Queries	Jonathan Zarecki, Shaul Markovitch	In order to minimize human labeling efforts, we propose a novel active learning solution that does not rely on existing sources of unlabeled data.
369	I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations	Xufang Luo, Qi Meng, Di He, Wei Chen, Yunhong Wang	Next, based on such observations, we define an indicator of the representations for DRL model, which is the Number of Significant Singular Values (NSSV) of a representation matrix. Then, we propose I4R algorithm, to improve DRL algorithms by adding the corresponding regularization term to enhance the NSSV.
370	Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)	Masataro Asai, Christian Muise	Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities.
371	Optimality, Accuracy, and E?iciency of an Exact Functional Test	Hien H. Nguyen, Hua Zhong, Mingzhou Song	Here, we prove the functional optimality of the EFT statistic, demonstrate its advantage in functional inference accuracy over five other methods, and develop a branch-and-bound algorithm with dynamic and quadratic programming to run at orders of magnitude faster than its previous implementation.
372	Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability	Deng Pan, Xiangrui Li, Xin Li, Dongxiao Zhu	We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss.
373	SVRG for Policy Evaluation with Fewer Gradient Evaluations	Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup	In this work, we show that two variants of SVRG for policy evaluation could significantly diminish the number of gradient calculations while preserving a linear convergence speed.
374	Diffusion Variational Autoencoders	Luis A. Perez Rey, Vlado Menkovski, Jim Portegies	To remove topological obstructions, we introduce Diffusion Variational Autoencoders (DeltaVAE) with arbitrary (closed) manifolds as a latent space.
375	Only Relevant Information Matters: Filtering Out Noisy Samples To Boost RL	Yannis Flet-Berliac, Philippe Preux	Following this line of thought, we introduce SAUNA, a method where non-informative transitions are rejected from the gradient update.
376	Consistent MetaReg: Alleviating Intra-task Discrepancy for Better Meta-knowledge	Pinzhuo Tian, Lei Qi, Shaokang Dong, Yinghuan Shi, Yang Gao	To overcome this limitation, we develop a new consistent meta-regularization method to reduce the intra-task data-distribution discrepancy.
377	Accelerating Stratified Sampling SGD by Reconstructing Strata	Weijie Liu, Hui Qian, Chao Zhang, Zebang Shen, Jiahao Xie, Nenggan Zheng	In this paper, a novel stratified sampling strategy is designed to accelerate the mini-batch SGD.
378	Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation	Ruobing Xie, Zhijie Qiu, Jun Rao, Yi Liu, Bo Zhang, Leyu Lin	To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels.
379	KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction	Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, Xiangxiang Zeng	To address the above limitations, we propose an end-to-end framework, called Knowledge Graph Neural Network (KGNN), to resolve the DDI prediction.
380	Toward a neuro-inspired creative decoder	Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah	Inspired by this seminal finding, in this study we propose a creative decoder within a deep generative framework, which involves direct modulation of the neuronal activation pattern after sampling from the learned latent space.
381	Inference-Masked Loss for Deep Structured Output Learning	Quan Guo, Hossein Rajaby Faghihi, Yue Zhang, Andrzej Uszok, Parisa Kordjamshidi	Compared to using conventional loss functions that penalize every local error independently, we propose an inference-masked loss that takes into account the effect of inference and does not penalize the local errors that can be corrected by the inference.
382	Deep Latent Low-Rank Fusion Network for Progressive Subspace Discovery	Zhao Zhang, Jiahuan Ren, Zheng Zhang, Guangcan Liu	In this paper, we present a new and effective strategy to extend the sin-gle-layer latent low-rank models into multi-ple-layers, and propose a new and progressive Deep Latent Low-Rank Fusion Network (DLRF-Net) to uncover deep features and struc-tures embedded in input data.
383	General Purpose MRF Learning with Neural Network Potentials	Hao Xiong, Nicholas Ruozzi	In this work, we propose a generic MLE estimation procedure for MRFs whose potential functions are modeled by neural networks.
384	Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications	Shujian Yu, Ammar Shaker, Francesco Alesiani, Jose Principe	We propose a simple yet powerful test statistic to quantify the discrepancy between two conditional distributions.
385	Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution	Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren	In this paper, we propose a new framework to incorporate the information from dynamic knowledge graphs for time series prediction.
386	Is the Skip Connection Provable to Reform the Neural Network Loss Landscape?	Lifu Wang, Bo Shen, Ning Zhao, Zhiyuan Zhang	In this paper, we follow this line to study the topology (sub-level sets) of the loss landscape of deep ReLU neural networks with a skip connection and theoretically prove that the skip connection network inherits the good properties of the two-layer network and skip connections can help to control the connectedness of the sub-level sets, such that any local minima worse than the global minima of some two-layer ReLU network will be very “shallow".
387	Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network	Haowen Fang, Amar Shrestha, Ziyi Zhao, Qinru Qiu	We proposed a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights.
388	Mutual Information Estimation using LSH Sampling	Ryan Spring, Anshumali Shrivastava	In this paper, we use locality-sensitive hashing (LSH) as an adaptive sampler and propose an unbiased estimator that accurately approximates the partition function in sub-linear (near-constant) time.
389	Reward Prediction Error as an Exploration Objective in Deep RL	Riley Simmons-Edler, Ben Eisner, Daniel Yang, Anthony Bisulco, Eric Mitchell, Sebastian Seung, Daniel Lee	We propose a new exploration objective, maximizing the reward prediction error (RPE) of a value function trained to predict extrinsic reward.
390	BRPO: Batch Residual Policy Optimization	Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed Chi, Craig Boutilier	To remedy this, we propose residual policies, where the allowable deviation of the learned policy is state-action-dependent.
391	Communicative Representation Learning on Attributed Molecular Graphs	Ying Song, Shuangjia Zheng, Zhangming Niu, Zhang-hua Fu, Yutong Lu, Yuedong Yang	Herein, we propose a Communicative Message Passing Neural Network (CMPNN) to improve the molecular embedding by strengthening the message interactions between nodes and edges through a communicative kernel.
392	Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error	Taiji Suzuki, Hiroshi Abe, Tomoya Murata, Shingo Horiuchi, Kotaro Ito, Tokuma Wachi, So Hirai, Masatoshi Yukishima, Tomoaki Nishimura	To resolve this issue, we develop a new theoretical framework for model compression and propose a new pruning method called {\it spectral pruning} based on this framework.
393	Reinforcement Learning Framework for Deep Brain Stimulation Study	Dmitrii Krylov, Remi Tachet des Combes, Romain Laroche, Michael Rosenblum, Dmitry V. Dylov	We present the first Reinforcement Learning (RL) gym framework that emulates this collective behavior of neurons and allows us to find suppression parameters for the environment of synthetic degenerate models of neurons.
394	DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization	Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura	To suggest an executable action for users, we propose a new framework of CE for extracting an action by evaluating its reality on the empirical data distribution.
395	Constrained Policy Improvement for Efficient Reinforcement Learning	Elad Sarafian, Aviv Tamar, Sarit Kraus	We propose a policy improvement algorithm for Reinforcement Learning (RL) termed Rerouted Behavior Improvement (RBI).
396	Adaptively Multi-Objective Adversarial Training for Dialogue Generation	Xuemiao Zhang, Zhouxing Tan, Xiaoning Zhang, Yang Cao, Rui Yan	To address it, we reframe the dialogue generation task as a multi-objective optimization problem and propose a novel adversarial dialogue generation framework with multiple discriminators that excel in different objectives for multiple linguistic aspects, called AMPGAN, whose feasibility is proved by theoretical derivations.
397	Multi-Class Imbalanced Graph Convolutional Network Learning	Min Shi, Yufei Tang, Xingquan Zhu, David Wilson, Jianxun Liu	In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning.
398	Learning in the Wild with Incremental Skeptical Gaussian Processes	Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini	In order to tackle this task, we propose a redesign of skeptical learning centered around Gaussian Processes (GPs).
399	Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling	Daniel Stoller, Mi Tian, Sebastian Ewert, Simon Dixon	For this, we use a U-Net architecture that computes features at multiple time-scales and adapt it to our auto-regressive scenario by making convolutions causal.
400	Independent Skill Transfer for Deep Reinforcement Learning	Qiangxing Tian, Guanchu Wang, Jinxin Liu, Donglin Wang, Yachen Kang	In this paper, we propose a novel efficient skill transfer method, where we learn independent skills and only independent components of skills are transferred instead of the whole set of skills.
401	Crowdsourcing with Multiple-Source Knowledge Transfer	Guangyang Han, Jinzheng Tu, Guoxian Yu, Jun Wang, Carlotta Domeniconi	To make this idea concrete, we introduce the Crowdsourcing with Multiple-source Knowledge Transfer (CrowdMKT)approach to transfer knowledge from multiple, similar, but different domains for a new task, and to reduce the negative impact of irrelevant sources.
402	Class Prior Estimation in Active Positive and Unlabeled Learning	Lorenzo Perini, Vincent Vercruyssen, Jesse Davis	In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning.
403	Reducing Underflow in Mixed Precision Training by Gradient Scaling	Ruizhe Zhao, Brian Vogel, Tanvir Ahmed, Wayne Luk	We propose gradient scaling, a novel method that analytically calculates the appropriate scale for each gradient on-the-fly.
404	Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets	Cong Fei, Bin Wang, Yuzheng Zhuang, Zongzhang Zhang, Jianye Hao, Hongbo Zhang, Xuewu Ji, Wulong Liu	In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary selector.
405	Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs	Bo Xue, Guanghui Wang, Yimu Wang, Lijun Zhang	In this paper, we study the problem of stochastic linear bandits with finite action sets.
406	Learning From Multi-Dimensional Partial Labels	Haobo Wang, Weiwei Liu, Yang Zhao, Tianlei Hu, Ke Chen, Gang Chen	In this paper, we propose a novel learning paradigm: MultiDimensional Partial Label Learning (MDPL) where the ground-truth labels of each instance are concealed in multiple candidate label sets.
407	Unsupervised Representation Learning by Predicting Random Distances	Hu Wang, Guansong Pang, Chunhua Shen, Congbo Ma	To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space.
408	Asymmetric Distribution Measure for Few-shot Learning	Wenbin Li, Lei Wang, Jing Huo, Yinghuan Shi, Yang Gao, Jiebo Luo	To that end, we propose a novel Asymmetric Distribution Measure (ADM) network for few-shot learning by calculating a joint local and global asymmetric measure between two multivariate local distributions of a query and a class.
409	Quadratic Sparse Gaussian Graphical Model Estimation Method for Massive Variables	Jiaqi Zhang, Meng Wang, Qinchi Li, Sen Wang, Xiaojun Chang, Beilun Wang	To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST).
410	Multi-View Attribute Graph Convolution Networks for Clustering	Jiafeng Cheng, Qianqian Wang, Zhiqiang Tao, Deyan Xie, Quanxue Gao	In this paper, we propose a novel Multi-View Attribute Graph Convolution Networks (MAGCN) model for the clustering task.
411	Classification with Rejection: Scaling Generative Classifiers with Supervised Deep Infomax	Xin Wang, Siu Ming Yiu	In this paper, we propose Supervised Deep InfoMax (SDIM), which introduces supervised probabilistic constraints to the encoder outputs.
412	TransRHS: A Representation Learning Method for Knowledge Graphs with Relation Hierarchical Structure	Fuxiang Zhang, Xin Wang, Zhao Li, Jianxin Li	In this paper, we propose a novel method named TransRHS, which is able to incorporate RHS seamlessly into the embeddings.
413	Towards Explainable Conversational Recommendation	Zhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, Enhong Chen	In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation.
414	A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation	Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Xiaolin Zheng	To address these challenges, in this paper, we propose a graphical and attentional framework, called GA-DTCDR.
415	Discriminative Feature Selection via A Structured Sparse Subspace Learning Module	Zheng Wang, Feiping Nie, Lai Tian, Rong Wang, Xuelong Li	Elicited by proposed module, we design a new discriminative feature selection method, named Subspace Sparsity Discriminant Feature Selection S^2DFS which enables the following new functionalities: 1) Proposed S^2DFS method directly joints trace ratio objective and structured sparse subspace constraint via L2,0-norm to learn a row-sparsity subspace, which improves the discriminability of model and overcomes the parameter-tuning trouble with comparison to the methods used L2,1-norm regularization; 2) An alternative iterative optimization algorithm based on the proposed S^3L module is presented to explicitly solve the proposed problem with a closed-form solution and strict convergence proof.
416	Evaluating and Aggregating Feature-based Model Explanations	Umang Bhatt, Adrian Weller, José M. F. Moura	This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity.
417	User Modeling with Click Preference and Reading Satisfaction for News Recommendation	Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang	In this paper we propose to model user interest from both click behaviors on news titles and reading behaviors on news content for news recommendation.
418	RDF-to-Text Generation with Graph-augmented Structural Neural Encoders	Hanning Gao, Lingfei Wu, Po Hu, Fangli Xu	To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples.
419	Hybrid Learning for Multi-agent Cooperation with Sub-optimal Demonstrations	Peixi Peng, Junliang Xing, Lili Cao	To learn better policies from these sub-optimal demonstrations, this paper follows a centralized learning and decentralized execution framework and proposes a novel hybrid learning method based on multi-agent actor-critic.
420	Bayesian Optimization using Pseudo-Points	Chao Qian, Hang Xiong, Ke Xue	In this paper, we propose a new general framework for BO by generating pseudo-points (i.e., data points whose objective values are not evaluated) to improve the GP model.
421	Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent	Bowen Weng, Huaqing Xiong, Yingbin Liang, Wei Zhang	To further improve the performance, we propose to incorporate the momentum restart scheme to Q-AMSGrad, resulting in the so-called Q-AMSGradR algorithm.
422	Discovering Latent Class Labels for Multi-Label Learning	Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi	In this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously.
423	MergeNAS: Merge Operations into One for Differentiable Architecture Search	Xiaoxing Wang, Chao Xue, Junchi Yan, Xiaokang Yang, Yonggang Hu, Kewei Sun	In this paper, we propose a one-shot neural architecture search method referred to as MergeNAS by merging different types of operations e.g. convolutions into one operation.
424	BaKer-Nets: Bayesian Random Kernel Mapping Networks	Hui Xue, Zheng-Fan Wu	Hence, in this paper, we propose the novel Bayesian random Kernel mapping Networks (BaKer-Nets) with preferable learning processes by escaping randomly from most local minima.
425	On Metric DBSCAN with Low Doubling Dimension	Hu Ding, Fan Yang, Mingyue Wang	In this paper, we consider the metric DBSCAN problem under the assumption that the inliers (excluding the outliers) have a low doubling dimension.
426	Multi-Feedback Bandit Learning with Probabilistic Contexts	Luting Yang, Jianyi Yang, Shaolei Ren	In this work, we focus on multi-feedback bandit learning with probabilistic contexts, where a bundle of contexts are revealed to the agent along with their corresponding probabilities at the beginning of each round.
427	Efficient Deep Reinforcement Learning via Adaptive Policy Transfer	Tianpei Yang, Jianye Hao, Zhaopeng Meng, Zongzhang Zhang, Yujing Hu, Yingfeng Chen, Changjie Fan, Weixun Wang, Wulong Liu, Zhaodong Wang, Jiajie Peng	In this paper, we propose a novel Policy Transfer Framework (PTF) by taking advantage of this idea.
428	Greedy Convex Ensemble	Thanh Tan Nguyen, Nan Ye, Peter Bartlett	We consider learning a convex combination of basis models, and present some new theoretical and empirical results that demonstrate the effectiveness of a greedy approach.
429	MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics	Mohammadamin Barekatain, Ryo Yonetani, Masashi Hamaya	We learn to aggregate the actions provided by the source policies adaptively to maximize the target task performance. Meanwhile, we learn an auxiliary network that predicts residuals around the aggregated actions, which ensures the target policy’s expressiveness even when some of the source policies perform poorly.
430	Gradient Perturbation is Underrated for Differentially Private Convex Optimization	Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu	In contrast, we explore how the privacy noise affects the optimization property.
431	Weakly-Supervised Multi-view Multi-instance Multi-label Learning	Yuying Xing, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang	To tackle such a weakly-supervised M3L task, a solution called WSM3L is introduced.
432	I²HRL: Interactive Influence-based Hierarchical Reinforcement Learning	Rundong Wang, Runsheng Yu, Bo An, Zinovi Rabinovich	To address these issues, we propose a novel HRL method: Interactive Influence-based Hierarchical Reinforcement Learning (I^2HRL).
433	A Dual Input-aware Factorization Machine for CTR Prediction	Wantong Lu, Yantao Yu, Yongzhe Chang, Zhen Wang, Chenhui Li, Bo Yuan	Inspired by the success of Input-aware Factorization Machines (IFMs), which aim to learn more flexible and informative representations of a given feature according to different input instances, we propose a novel model named Dual Input-aware Factorization Machines (DIFMs) that can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously.
434	Dual Policy Distillation	Kwei-Herng Lai, Daochen Zha, Yuening Li, Xia Hu	In this work, we introduce dual policy distillation (DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.
435	Exploring Parameter Space with Structured Noise for Meta-Reinforcement Learning	Hui Xu, Chong Zhang, Jiaxing Wang, Deqiang Ouyang, Yu Zheng, Jie Shao	In this paper, we investigate whether previous learning experiences can be leveraged to guide exploration of current new task.
436	Semi-supervised Clustering via Pairwise Constrained Optimal Graph	Feiping Nie, Han Zhang, Rong Wang, Xuelong Li	In this paper, we present a technique of definitely addressing the pairwise constraints in the semi-supervised clustering.
437	Generating Robust Audio Adversarial Examples with Temporal Dependency	Hongting Zhang, Pan Zhou, Qiben Yan, Xiao-Yang Liu	In this paper, we propose a new Iterative Proportional Clipping (IPC) algorithm that preserves temporal dependency in audios for generating more robust adversarial examples.
438	BERT-INT:A BERT-based Interaction Model For Knowledge Graph Alignment	Xiaobin Tang, Jing Zhang, Bo Chen, Yang Yang, Hong Chen, Cuiping Li	This work presents an interaction model to only leverage the side information.
439	Self-adaptive Re-weighted Adversarial Domain Adaptation	Shanshan Wang, Lei Zhang	To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution.
440	One-Shot Neural Architecture Search via Novelty Driven Sampling	Miao Zhang, Huiqi Li, Shirui Pan, Taoping Liu, Steven Su	To handle this deceptive problem, this paper presents a new approach, Efficient Novelty-driven Neural Architecture Search, to sample the most abnormal architecture to train the supernet.
441	Split to Be Slim: An Overlooked Redundancy in Vanilla Convolution	Qiulin Zhang, Zhuqing Jiang, Qishuo Lu, Jia’nan Han, Zhengxin Zeng, Shanghua Gao, Aidong Men	Specifically, we split input feature maps into the representative part and the uncertain redundant part, where intrinsic information is extracted from the representative part through relatively heavy computation while tiny hidden details in the uncertain redundant part are processed with some light-weight operation. To recalibrate and fuse these two groups of processed features, we propose a parameters-free feature fusion module.
442	Joint Multi-view 2D Convolutional Neural Networks for 3D Object Classification	Jinglin Xu, Xiangsen Zhang, Wenbin Li, Xinwang Liu, Junwei Han	To this end, this work proposes a novel multi-view framework by jointly using multiple 2D-CNNs to capture discriminative information with relationships as well as a new multi-view loss fusion strategy, in an end-to-end manner.
443	Trajectory Similarity Learning with Auxiliary Supervision and Optimal Matching	Hanyuan Zhang, Xinyu Zhang, Qize Jiang, Baihua Zheng, Zhenbang Sun, Weiwei Sun, Changhu Wang	In this paper, we propose a novel trajectory representation learning framework Traj2SimVec that performs scalable and robust trajectory similarity computation.
444	Discovering Subsequence Patterns for Next POI Recommendation	Kangzhi Zhao, Yong Zhang, Hongzhi Yin, Jin Wang, Kai Zheng, Xiaofang Zhou, Chunxiao Xing	In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns.
445	Label Enhancement for Label Distribution Learning via Prior Knowledge	Yongbiao Gao, Yu Zhang, Xin Geng	In this paper, we formulate the label enhancement as a dynamic decision process.
446	CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network	Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, Guo-Sen Xie	In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues.
447	P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection	Zhiwei Zhang, Shifeng Chen, Lei Sun	In this paper, the Generative Adversarial Networks (GANs) based on encoder-decoder-encoder pipeline are used for detection and achieve state-of-the-art performance.
448	EndCold: An End-to-End Framework for Cold Question Routing in Community Question Answering Services	Jiankai Sun, Jie Zhao, Huan Sun, Srinivasan Parthasarathy	Therefore, we propose to address the challenge of leveraging heterogeneous graph and textual information for cold question routing by designing an end-to-end framework that jointly learns CQA node embeddings and finds best answerers for cold questions.
449	Multi-Scale Group Transformer for Long Sequence Modeling in Speech Separation	Yucheng Zhao, Chong Luo, Zheng-Jun Zha, Wenjun Zeng	In this paper, we introduce Transformer to the time-domain methods for single-channel speech separation.
450	pbSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization	Beitong Zhou, Jun Liu, Weigao Sun, Ruijuan Chen, Claire Tomlin, Ye Yuan	We propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term pbSGD.
451	Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks	Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu	In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted".
452	Tight Convergence Rate of Gradient Descent for Eigenvalue Computation	Qinghua Ding, Kaiwen Zhou, James Cheng	In this paper, we show that RGD in fact converges at rate O(log(n/epsilon)/Delta), and give instances to shows the tightness of our result.
453	Smart Contract Vulnerability Detection using Graph Neural Network	Yuan Zhuang, Zhenguang Liu, Peng Qian, Qi Liu, Xiang Wang, Qinming He	In this paper, we explore using graph neural networks (GNNs) for smart contract vulnerability detection.
454	Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation	Danbing Zou, Qikui Zhu, Pingkun Yan	In this paper, we propose an innovative Dual-Scheme Fusion Network (DSFN) for unsupervised domain adaptation.
455	Neighbor Combinatorial Attention for Critical Structure Mining	Tanli Zuo, Yukun Qiu, Wei-Shi Zheng	In this work, we propose a novel GCN module named Neighbor Combinatorial ATtention (NCAT) to find critical structure in graph-structured data.
456	Inferring Degrees from Incomplete Networks and Nonlinear Dynamics	Chunheng Jiang, Jianxi Gao, Malik Magdon-Ismail	We combine the mean-field theory with combinatorial optimization to learn vertex degrees.
457	DeepWeave: Accelerating Job Completion Time with Deep Reinforcement Learning-based Coflow Scheduling	Penghao Sun, Zehua Guo, Junchao Wang, Junfei Li, Julong Lan, Yuxiang Hu	Different from existing schemes that use hand-crafted heuristic algorithms to solve this problem, in this paper, we propose a Deep Reinforcement Learning (DRL) framework named DeepWeave to generate coflow scheduling policies.
458	Adversarial Mutual Information Learning for Network Embedding	Dongxiao He, Lu Zhai, Zhigang Li, Di Jin, Liang Yang, Yuxiao Huang, Philip S. Yu	To address this problem, we propose to use the adversarial idea on the representation mechanism, i.e. on the encoding mechanism under the framework of autoencoder.
459	Binary Classification from Positive Data with Skewed Confidence	Kazuhiko Shinoda, Hirotaka Kaji, Masashi Sugiyama	In this paper, we introduce the parameterized model of the skewed confidence, and propose the method for selecting the hyperparameter which cancels out the negative impact of the skewed confidence under the assumption that we have the misclassification rate of positive samples as a prior knowledge.
460	A Label Attention Model for ICD Coding from Clinical Text	Thanh Vu, Dat Quoc Nguyen, Anthony Nguyen	In this paper, we propose a new label attention model for automatic ICD coding, which can handle both the various lengths and the interdependence of the ICD code related text fragments.
461	Predicting Landslides Using Locally Aligned Convolutional Neural Networks	Ainaz Hajimoradlou, Gioachino Roberti, David Poole	We propose a model called Locally Aligned Convolutional Neural Network, LACNN, that follows the ground surface at multiple scales to predict possible landslide occurrence for a single point.
462	Semi-Markov Reinforcement Learning for Stochastic Resource Collection	Sebastian Schmoll, Matthias Schubert	We show that the task of collecting stochastic, spatially distributed resources (Stochastic Resource Collection, SRC) may be considered as a Semi-Markov-Decision-Process.
463	Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players	Yudong Luo, Oliver Schulte, Pascal Poupart	This paper combines Q-function learning with inverse reinforcement learning (IRL) to provide a novel player ranking method.
464	C3MM: Clique-Closure based Hyperlink Prediction	Govind Sharma, Prasanna Patil, M. Narasimha Murty	Akin to the link prediction problem in graphs, we deal with hyperlink (higher-order link) prediction in hypergraphs.
465	Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning	Ruimin Shen, Yan Zheng, Jianye Hao, Zhaopeng Meng, Yingfeng Chen, Changjie Fan, Yang Liu	To bridge the gap, we introduce a new framework, named EMOGI, which can automatically generate desirable styles with almost no domain knowledge.
466	CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning	Yuanrui Dong, Peng Zhao, Hanqiao Yu, Cong Zhao, Shusen Yang	Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL.
467	HID: Hierarchical Multiscale Representation Learning for Information Diffusion	Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia	In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.
468	The Graph-based Mutual Attentive Network for Automatic Diagnosis	Quan Yuan, Jun Chen, Chao Lu, Haifeng Huang	In this paper, we propose to build the diagnosis model based on the high-standard EMR documents from real hospitals to improve the accuracy and the credibility of the resulting model.
469	Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips	Ke Li, Lisi Chen, Shuo Shang	We investigate the problem of optimal route planning for massive-scale trips: Given a traffic-aware road network and a set of trip queries Q, we aim to find a route for each trip such that the global travel time cost for all queries in Q is minimized.
470	Automatic Emergency Diagnosis with Knowledge-Based Tree Decoding	Ke Wang, Xuyan Chen, Ning Chen, Ting Chen	In this paper, we propose a knowledge-based tree decoding model (K-BTD), and the inference procedure is a top-down decoding process from the root node to leaf nodes.
471	Exploiting Mutual Information for Substructure-aware Graph Representation Learning	Pengyang Wang, Yanjie Fu, Yuanchun Zhou, Kunpeng Liu, Xiaolin Li, Kien Hua	In this paper, we design and evaluate a new substructure-aware Graph Representation Learning (GRL) approach.
472	Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems	Seyed Iman Mirzadeh, Hassan Ghasemzadeh	In an entirely new direction, we introduce an effective method to find a combination of "multiple" models that are optimal in terms of power-efficiency and performance by solving an optimization problem in which both performance and power consumption are taken into account.
473	A Two-Stage Matheuristic Algorithm for Classical Inventory Routing Problem	Zhouxing Su, Shihao Huang, Chungen Li, Zhipeng Lü	We propose a two-stage matheuristic (TSMH) algorithm to solve the IRP.
474	Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising	Xiaotian Hao, Junqi Jin, Jianye Hao, Jin Li, Weixun Wang, Yi Ma, Zhenzhe Zheng, Han Li, Jian Xu, Kun Gai	To address this issue, based on a key observation that the matching instances vary not too much, we propose NeuSearcher which leverage the knowledge learned from previously instances to solve new problem instances.
475	FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces	Run Wang, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yihao Huang, Jian Wang, Yang Liu	In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces.
476	Learning Data-Driven Drug-Target-Disease Interaction via Neural Tensor Network	Huiyuan Chen, Jing Li	Here we propose a Neural Tensor Network (NeurTN) to assist personalized medicine treatments.
477	Why We Go Where We Go: Profiling User Decisions on Choosing POIs	Renjun Hu, Xinjiang Lu, Chuanren Liu, Yanyan Li, Hao Liu, Jingjing Gu, Shuai Ma, Hui Xiong	To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people’s decisions on choosing POIs.
478	MLS3RDUH: Deep Unsupervised Hashing via Manifold based Local Semantic Similarity Structure Reconstructing	Rong-Cheng Tu, Xian-Ling Mao, Wei Wei	Thus, to tackle this problem, in this paper, we propose a novel deep unsupervised hashing method, called MLS3RDUH, which can reduce the noisy datapoints to further enhance retrieval performance.
479	A Game Theoretic Approach For Core Resilience	Sourav Medya, Tiyani Ma, Arlei Silva, Ambuj Singh	Motivated by these challenges, we propose a novel algorithm inspired by Shapley value—a cooperative game-theoretic concept— that is able to leverage the strong interdependencies in the effects of edge removals in the search space.
480	Differential Privacy for Stackelberg Games	Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck	This paper introduces a differentially private (DP) mechanism to protect the information exchanged during the coordination of sequential and interdependent markets.
481	HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural Network	Rui Liu, Huilin Peng, Yong Chen, Dell Zhang	In this paper, we propose a novel approach dubbed HyperNews which explicitly models the effect of timeliness on news recommendation.
482	Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles	Andrea Piazzoni, Jim Cherian, Martin Slavik, Justin Dauwels	In this paper, we propose a simulation-based methodology towards answering this question.
483	BERT-PLI: Modeling Paragraph-Level Interactions for Legal Case Retrieval	Yunqiu Shao, Jiaxin Mao, Yiqun Liu, Weizhi Ma, Ken Satoh, Min Zhang, Shaoping Ma	To address these challenges, we propose BERT-PLI, a novel model that utilizes BERT to capture the semantic relationships at the paragraph-level and then infers the relevance between two cases by aggregating paragraph-level interactions.
484	Auxiliary Template-Enhanced Generative Compatibility Modeling	Jinhuan Liu, Xuemeng Song, Zhaochun Ren, Liqiang Nie, Zhaopeng Tu, Jun Ma	In this work, we aim to improve the compatibility modeling by sketching a compatible template for a given item as an auxiliary link between fashion items.
485	Community-Centric Graph Convolutional Network for Unsupervised Community Detection	Dongxiao He, Yue Song, Di Jin, Zhiyong Feng, Binbin Zhang, Zhizhi Yu, Weixiong Zhang	We introduced a community-centric dual decoder to reconstruct network structures and node attributes separately in an unsupervised fashion, for faithful community detection in the input space.
486	An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration	Yumin Su, Liang Zhang, Quanyu Dai, Bo Zhang, Jinyao Yan, Dan Wang, Yongjun Bao, Sulong Xu, Yang He, Weipeng Yan	In this paper, we propose a novel deep learning framework to tackle the two challenges.
487	Learning Model with Error — Exposing the Hidden Model of BAYHENN	Harry W. H. Wong, Jack P. K. Ma, Donald P. H. Wong, Lucien K. L. Ng, Sherman S. M. Chow	This paper refutes the security claim of BAYHENN via both theoretical and empirical analyses.
488	Learning the Compositional Visual Coherence for Complementary Recommendations	Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, Tao Mei	Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents.
489	Efficient Community Search over Large Directed Graph: An Augmented Index-based Approach	Yankai Chen, Jie Zhang, Yixiang Fang, Xin Cao, Irwin King	To tackle this issue, in this paper we propose a novel index called D-Forest, which allows a CSD query to be completed within the optimal time cost.
490	An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins	Lu Zhang, Zhu Sun, Jie Zhang, Yu Lei, Chen Li, Ziqing Wu, Horst Kloeden, Felix Klanner	To ease this issue, we propose a novel interactive multi-task learning (iMTL) framework to better exploit the interplay between activity and location preference.
491	Pivot-based Maximal Biclique Enumeration	Aman Abidi, Rui Zhou, Lu Chen, Chengfei Liu	Therefore, in this paper, we explore the pivot-based pruning for biclique enumeration.
492	CooBa: Cross-project Bug Localization via Adversarial Transfer Learning	Ziye Zhu, Yun Li, Hanghang Tong, Yu Wang	To address this issue, we propose an adversarial transfer learning bug localization approach, focusing on only transferring the common characteristics (i.e., public information) across projects.
493	Hierarchical Linear Disentanglement of Data-Driven Conceptual Spaces	Rana Alshaikh, Zied Bouraoui, Steven Schockaert	Our hypothesis in this paper is that such single-space representations are sub-optimal for learning quality dimensions, due to the fact that semantic features are often only relevant to a subset of the entities.
494	How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context	Qian Liu, Bei Chen, Jiaqi Guo, Jian-Guang Lou, Bin Zhou, Dongmei Zhang	We present a grammar-based decoding semantic parser and adapt typical context modeling methods on top of it.
495	Lexical-Constraint-Aware Neural Machine Translation via Data Augmentation	Guanhua Chen, Yun Chen, Yong Wang, Victor O.K. Li	In response to these problems, we present a conceptually simple and empirically effective data augmentation approach in lexical constrained neural machine translation.
496	Attention-based Multi-level Feature Fusion for Named Entity Recognition	Zhiwei Yang, Hechang Chen, Jiawei Zhang, Jing Ma, Yi Chang	In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER.
497	Exemplar Guided Neural Dialogue Generation	Hengyi Cai, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Dawei Yin	To address the issues, we propose an exemplar guided neural dialogue generation model where exemplar responses are retrieved in terms of both the text similarity and the topic proximity through a two-stage exemplar retrieval model.
498	Knowledge Enhanced Event Causality Identification with Mention Masking Generalizations	Jian Liu, Yubo Chen, Jun Zhao	In this paper, we present a new method for event causality identification, aiming to address limitations of previous methods.
499	Two-Phase Hypergraph Based Reasoning with Dynamic Relations for Multi-Hop KBQA	Jiale Han, Bo Cheng, Xu Wang	Inspired by human’s hop-by-hop reasoning, we propose an interpretable KBQA model based on DHGCN, namely two-phase hypergraph based reasoning with dynamic relations, which explicitly updates relation information and dynamically pays attention to different relations at different hops.
500	LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning	Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, Yue Zhang	We build a comprehensive dataset, named LogiQA, which is sourced from expert-written questions for testing human Logical reasoning.
501	Guided Generation of Cause and Effect	Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, Benjamin Van Durme	We present a conditional text generation framework that posits sentential expressions of possible causes and effects.
502	EmoElicitor: An Open Domain Response Generation Model with User Emotional Reaction Awareness	Shifeng Li, Shi Feng, Daling Wang, Kaisong Song, Yifei Zhang, Weichao Wang	In this paper, we propose a novel variational model named EmoElicitor to generate appropriate responses that can elicit user’s specific emotion.
503	RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task	Yu Zeng, Yan Gao, Jiaqi Guo, Bei Chen, Qian Liu, Jian-Guang Lou, Fei Teng, Dongmei Zhang	In this paper, we propose a novel recursive semantic parsing framework called RECPARSER to generate the nested SQL query layer-by-layer.
504	Learning Latent Forests for Medical Relation Extraction	Zhijiang Guo, Guoshun Nan, Wei LU, Shay B. Cohen	In this work, we propose a novel model which treats the dependency structure as a latent variable and induces it from the unstructured text in an end-to-end fashion.
505	Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment	Hao Nie, Xianpei Han, Le Sun, Chi Man Wong, Qiang Chen, Suhui Wu, Wei Zhang	In this paper, we propose to jointly leverage the global KG structure and entity-specific relational triples for better entity alignment.
506	Hierarchical Matching Network for Heterogeneous Entity Resolution	Cheng Fu, Xianpei Han, Jiaming He, Le Sun	To resolve the above problems, this paper proposes an end-to-end hierarchical matching network (HierMatcher) for entity resolution, which can jointly match entities in three levels—token, attribute, and entity.
507	Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model	Juntao Li, Ruidan He, Hai Ye, Hwee Tou Ng, Lidong Bing, Rui Yan	Specifically, we propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features and domain-invariant features from the entangled pretrained cross-lingual representations, given unlabeled raw texts in the source language.
508	Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning	Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Wei Wu	In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system’s performance with respect to the produced answers.
509	Generating Reasonable Legal Text through the Combination of Language Modeling and Question Answering	Weijing Huang, Xianfeng Liao, Zhiqiang Xie, Jiang Qian, Bojin Zhuang, Shaojun Wang, Jing Xiao	To generate reasonable legal documents, we propose a novel method CoLMQA, which (1) combines Language Modeling and Question Answering, (2) generates text with slots by Language Modeling, and (3) fills the slots by our proposed Question Answering method named Transformer-based Key-Value Memory Networks.
510	Modeling Voting for System Combination in Machine Translation	Xuancheng Huang, Jiacheng Zhang, Zhixing Tan, Derek F. Wong, Huanbo Luan, Jingfang Xu, Maosong Sun, Yang Liu	In this work, we propose an approach to modeling voting for system combination in machine translation.
511	Unsupervised Multilingual Alignment using Wasserstein Barycenter	Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu	Instead of going through a rather arbitrarily chosen pivot language, we propose to use the Wasserstein barycenter as a more informative “mean” language: it encapsulates information from all languages and minimizes all pairwise transportation costs.
512	A De Novo Divide-and-Merge Paradigm for Acoustic Model Optimization in Automatic Speech Recognition	Conghui Tan, Di Jiang, Jinhua Peng, Xueyang Wu, Qian Xu, Qiang Yang	In this paper, we propose a novel Divide-and-Merge paradigm to solve salient problems plaguing the ASR field.
513	Neural Abstractive Summarization with Structural Attention	Tanya Chowdhury, Sachin Kumar, Tanmoy Chakraborty	In this work, we present a hierarchical encoder based on structural attention to model such inter-sentence and inter-document dependencies.
514	Domain Adaptation for Semantic Parsing	Zechang Li, Yuxuan Lai, Yansong Feng, Dongyan Zhao	In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain.
515	Evaluating Natural Language Generation via Unbalanced Optimal Transport	Yimeng Chen, Yanyan Lan, Ruinbin Xiong, Liang Pang, Zhiming Ma, Xueqi Cheng	In this paper, we provide an in-depth study on the relations between these metrics.
516	Modeling Topical Relevance for Multi-Turn Dialogue Generation	Hainan Zhang, Yanyan Lan, Liang Pang, Hongshen Chen, Zhuoye Ding, Dawei Yin	In this paper, we propose a new model, named STAR-BTM, to tackle this problem.
517	Robust Front-End for Multi-Channel ASR using Flow-Based Density Estimation	Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Hyung Yong Kim, Nam Soo Kim	In this paper, we propose a novel approach which incorporates flow-based density estimation for the robust front-end using non-parallel clean and noisy speech.
518	Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base	Yongrui Chen, Huiying Li, Yuncheng Hua, Guilin Qi	In this paper, we propose a new formal query building approach that consists of two stages.
519	Towards Fully 8-bit Integer Inference for the Transformer Model	Ye Lin, Yanyang Li, Tengbo Liu, Tong Xiao, Tongran Liu, Jingbo Zhu	In this work, we show that after a principled modification on the Transformer architecture, dubbed Integer Transformer, an (almost) fully 8-bit integer inference algorithm Scale Propagation could be derived.
520	TopicKA: Generating Commonsense Knowledge-Aware Dialogue Responses Towards the Recommended Topic Fact	Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu	In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA.
521	Infobox-to-text Generation with Tree-like Planning based Attention Network	Yang Bai, Ziran Li, Ning Ding, Ying Shen, Hai-Tao Zheng	To address these issues, we propose a Tree-like PLanning based Attention Network (Tree-PLAN) which leverages both static order-planning and dynamic tuning to guide the generation.
522	Triple-to-Text Generation with an Anchor-to-Prototype Framework	Ziran Li, Zibo Lin, Ning Ding, Hai-Tao Zheng, Ying Shen	In this paper, we propose a novel anchor-to-prototype framework to bridge the gap between structured RDF triples and natural text.
523	Attention as Relation: Learning Supervised Multi-head Self-Attention for Relation Extraction	Jie Liu, Shaowei Chen, Bingquan Wang, Jiaxin Zhang, Na Li, Tong Xu	In this paper, we propose an attention-based joint model, which mainly contains an entity extraction module and a relation detection module, to address the challenges.
524	An Iterative Multi-Source Mutual Knowledge Transfer Framework for Machine Reading Comprehension	Xin Liu, Kai Liu, Xiang Li, Jinsong Su, Yubin Ge, Bin Wang, Jiebo Luo	In this paper, we propose a novel iterative multi-source mutual knowledge transfer framework for MRC.
525	Text Style Transfer via Learning Style Instance Supported Latent Space	Xiaoyuan Yi, Zhenghao Liu, Wenhao Li, Maosong Sun	To address this problem, we propose a style instance supported method, StyIns.
526	Learning with Noise: Improving Distantly-Supervised Fine-grained Entity Typing via Automatic Relabeling	Haoyu Zhang, Dingkun Long, Guangwei Xu, Muhua Zhu, Pengjun Xie, Fei Huang, Ji Wang	In this paper, we propose a probabilistic automatic relabeling method which treats all training samples uniformly.
527	Joint Time-Frequency and Time Domain Learning for Speech Enhancement	Chuanxin Tang, Chong Luo, Zhiyuan Zhao, Wenxuan Xie, Wenjun Zeng	In this paper, we present a cross-domain framework named TFT-Net, which takes time-frequency spectrogram as input and produces time-domain waveform as output.
528	Answer Generation through Unified Memories over Multiple Passages	Makoto Nakatsuji, Sohei Okui	First, it determines which tokens in the passages are matched to the question. In particular, it investigates matches between tokens in positive passages, which are assigned to the question, and those in negative passages, which are not related to the question. Next, it determines which tokens in the passage are matched to other passages assigned to the same question and at the same time it investi- gates the topics in which they are matched. Finally, it encodes the token sequences with the above two matching results into unified memories in the pas- sage encoders and learns the answer sequence by using an encoder-decoder with a multiple-pointer- generator mechanism.
529	On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification	Xin Liu, Jiefu Ou, Yangqiu Song, Xin Jiang	We argue that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis. We propose a novel model to combine these modules together.
530	MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation	Edoardo Barba, Luigi Procopio, Niccolò Campolungo, Tommaso Pasini, Roberto Navigli	In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones.
531	Dialogue State Induction Using Neural Latent Variable Models	Qingkai Min, Libo Qin, Zhiyang Teng, Xiao Liu, Yue Zhang	We propose the task of dialogue state induction, building two neural latent variable models that mine dialogue states automatically from unlabeled customer service dialogue records.
532	CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP	Libo Qin, Minheng Ni, Yue Zhang, Wanxiang Che	We propose a data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT, which encourages model to align representations from source and multiple target languages once by mixing their context information.
533	Task-Level Curriculum Learning for Non-Autoregressive Neural Machine Translation	Jinglin Liu, Yi Ren, Xu Tan, Chen Zhang, Tao Qin, Zhou Zhao, Tie-Yan Liu	To smooth the shift from AT training to NAT training, in this paper, we introduce semi-autoregressive translation (SAT) as intermediate tasks.
534	End-to-End Transition-Based Online Dialogue Disentanglement	Hui Liu, Zhan Shi, Jia-Chen Gu, Quan Liu, Si Wei, Xiaodan Zhu	In this paper, we introduce the first end-to- end transition-based model for online dialogue disentanglement.
535	Hierarchical Multi-task Learning for Organization Evaluation of Argumentative Student Essays	Wei Song, Ziyao Song, Lizhen Liu, Ruiji Fu	In contrast, we propose a neural hierarchical multi-task learning approach for jointly optimizing sentence and paragraph level discourse element identification and organization evaluation.
536	Transformers as Soft Reasoners over Language	Peter Clark, Oyvind Tafjord, Kyle Richardson	This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing a formal representation.
537	Neural Machine Translation with Error Correction	Kaitao Song, Xu Tan, Jianfeng Lu	In this paper, we introduce an error correction mechanism into NMT, which corrects the error information in the previous generated tokens to better predict the next token.
538	Alleviate Dataset Shift Problem in Fine-grained Entity Typing with Virtual Adversarial Training	Haochen Shi, Siliang Tang, Xiaotao Gu, Bo Chen, Zhigang Chen, Jian Shao, Xiang Ren	In this work, complementary to the label perspective, we reconsider this problem from the model perspective: Can we learn a more robust typing model with the existence of dataset shift?
539	Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network	Zeyun Tang, Yongliang Shen, Xinyin Ma, Wei Xu, Jiale Yu, Weiming Lu	In this paper, we propose a novel approach to tackle this multi-hop reading comprehension problem.
540	Gaussian Embedding of Linked Documents from a Pretrained Semantic Space	Antoine Gourru, Julien Velcin, Julien Jacques	We formulate the problem in such a way that we model each document as a Gaussian distribution in the word vector space.
541	Better AMR-To-Text Generation with Graph Structure Reconstruction	Tianming Wang, Xiaojun Wan, Shaowei Yao	In this paper, we propose a novel approach that generates texts from AMR graphs while reconstructing the input graph structures.
542	UniTrans : Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data	Qianhui Wu, Zijia Lin, Börje F. Karlsson, Biqing Huang, Jian-Guang Lou	In this paper, we find that both method types can complement each other, in the sense that, the former can exploit context information via language-independent features but sees no task-specific information in the target language; while the latter generally generates pseudo target-language training data via translation but its exploitation of context information is weakened by inaccurate translations.
543	Efficient Context-Aware Neural Machine Translation with Layer-Wise Weighting and Input-Aware Gating	Hongfei Xu, Deyi Xiong, Josef van Genabith, Qiuhui Liu	In this paper, we propose to make the most of layers pre-trained on sentence-level data in contextual representation learning, reusing representations from the sentence-level Transformer and significantly reducing the cost of incorporating contexts in translation.
544	Enhancing Dialog Coherence with Event Graph Grounded Content Planning	Jun Xu, Zeyang Lei, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che	In this paper, to enhance multi-turn dialog coherence, we propose to leverage event chains to help determine a sketch of a multi-turn dialog.
545	Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction	Tianyang Zhao, Zhao Yan, Yunbo Cao, Zhoujun Li	In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering.
546	Exploring Bilingual Parallel Corpora for Syntactically Controllable Paraphrase Generation	Mingtong Liu, Erguang Yang, Deyi Xiong, Yujie Zhang, Chen Sheng, Changjian Hu, Jinan Xu, Yufeng Chen	In this paper, we propose a novel end-to-end framework to leverage existing large-scale bilingual parallel corpora to generate paraphrases under the control of syntactic exemplars.
547	A Structured Latent Variable Recurrent Network With Stochastic Attention For Generating Weibo Comments	Shijie Yang, Liang Li, Shuhui Wang, Weigang Zhang, Qingming Huang, Qi Tian	In this paper, we propose a structured latent variable recurrent network, which exploits the hierarchical-structured latent variables with stochastic attention to model the variations of comments.
548	Dataless Short Text Classification Based on Biterm Topic Model and Word Embeddings	Yi Yang, Hongan Wang, Jiaqi Zhu, Yunkun Wu, Kailong Jiang, Wenli Guo, Wandong Shi	In this paper, we at first propose a novel model named Seeded Biterm Topic Model (SeedBTM) extending BTM to solve the problem of dataless short text classification with seed words.
549	Leveraging Document-Level Label Consistency for Named Entity Recognition	Tao Gui, Jiacheng Ye, Qi Zhang, Yaqian Zhou, Yeyun Gong, Xuanjing Huang	This work introduces a novel two-stage label refinement approach to handle document-level label consistency, where a key-value memory network is first used to record draft labels predicted by the base model, and then a multi-channel Transformer makes refinements on these draft predictions based on the explicit co-occurrence relationship derived from the memory network.
550	Towards Making the Most of Context in Neural Machine Translation	Zaixiang Zheng, Xiang Yue, Shujian Huang, Jiajun Chen, Alexandra Birch	We argue that previous research did not make a clear use of the global context, and propose a new document-level NMT framework that deliberately models the local context of each sentence with the awareness of the global context of the document in both source and target languages.
551	Hype-HAN: Hyperbolic Hierarchical Attention Network for Semantic Embedding	Chengkun Zhang, Junbin Gao	This paper bridges hyperbolic space’s superiority to the power-law structure of documents by introducing a hyperbolic neural network architecture named Hyperbolic Hierarchical Attention Network (Hype-HAN).
552	ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation	Dongling Xiao, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang	To address this issue, we propose an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method.
553	TransOMCS: From Linguistic Graphs to Commonsense Knowledge	Hongming Zhang, Daniel Khashabi, Yangqiu Song, Dan Roth	In this paper, we explore a practical way of mining commonsense knowledge from linguistic graphs, with the goal of transferring cheap knowledge obtained with linguistic patterns into expensive commonsense knowledge.
554	Teacher-Student Networks with Multiple Decoders for Solving Math Word Problem	Jipeng Zhang, Roy Ka-Wei Lee, Ee-Peng Lim, Wei Qin, Lei Wang, Jie Shao, Qianru Sun	To address this problem, this paper proposes a novel approach, TSN-MD, by leveraging the teacher network to integrate the knowledge of equivalent solution expressions and then to regularize the learning behavior of the student network.
555	Generalized Zero-Shot Text Classification for ICD Coding	Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing	In this paper, we propose a latent feature generation framework to improve the prediction on unseen codes without compromising the performance on seen codes.
556	Gated POS-Level Language Model for Authorship Verification	Linshu Ouyang, Yongzheng Zhang, Hui Liu, Yige Chen, Yipeng Wang	This paper introduces a novel POS-level (Part of Speech) gated RNN based language model to effectively learn the author-specific syntactic styles.
557	Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction	Shan Zhao, Minghao Hu, Zhiping Cai, Fang Liu	In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction.
558	Knowledge Graphs Enhanced Neural Machine Translation	Yang Zhao, Jiajun Zhang, Yu Zhou, Chengqing Zong	To improve the translation quality of these entities, in this paper we propose a novel KGs enhanced NMT method.
559	Fast and Accurate Neural CRF Constituency Parsing	Yu Zhang, Houquan Zhou, Zhenghua Li	This work presents a fast and accurate neural CRF constituency parser.
560	A Relation-Specific Attention Network for Joint Entity and Relation Extraction	Yue Yuan, Xiaofei Zhou, Shirui Pan, Qiannan Zhu, Zeliang Song, Li Guo	In this paper, we propose a relation-specific attention network (RSAN) to handle the issue.
561	Multi-Directional Heuristic Search	Dor Atzmon, Jiaoyang Li, Ariel Felner, Eliran Nachmani, Shahaf Shperberg, Nathan Sturtevant, Sven Koenig	In this paper, we introduce MM*, a Multi-Directional Heuristic Search algorithm that finds the optimal meeting location under different cost functions.
562	Steady-State Policy Synthesis in Multichain Markov Decision Processes	George Atia, Andre Beckus, Ismail Alkhouri, Alvaro Velasquez	In this paper, we provide a solution to the latter within the context of multichain MDPs over a class of policies that account for all possible transitions in the given MDP.
563	Delete- and Ordering-Relaxation Heuristics for HTN Planning	Daniel Höller, Pascal Bercher, Gregor Behnke	We introduce the problem class of delete- and ordering-free HTN planning as basis for novel HTN heuristics and show that its plan existence problem is still NP-complete.
564	Iterative-Deepening Conflict-Based Search	Eli Boyarski, Ariel Felner, Daniel Harabor, Peter J. Stuckey, Liron Cohen, Jiaoyang Li, Sven Koenig	In this paper, we present IDCBS, an iterative-deepening variant of CBS which can be executed without exhausting the memory and without strict time limits.
565	Plan-Space Explanation via Plan-Property Dependencies: Faster Algorithms & More Powerful Properties	Rebecca Eifler, Marcel Steinmetz, Álvaro Torralba, Jörg Hoffmann	First, we introduce new algorithms for computing plan-property dependencies, leveraging symbolic search and devising pruning methods for this purpose. Second, while the properties p were previously limited to goal facts and so-called action-set (AS) properties, here we extend them to LTL.
566	Front-to-Front Heuristic Search for Satisficing Classical Planning	Ryo Kuroiwa, Alex Fukunaga	In this paper, we propose Top-to-Top Bidirectional Search (TTBS), a new bidirectional search strategy with front-to-front heuristic evaluation.
567	Online Revenue Maximization for Server Pricing	Shant Boodaghians, Federico Fusco, Stefano Leonardi, Yishay Mansour, Ruta Mehta	This paper considers online revenue maximization for a unit capacity server, when jobs are non preemptive, in the Bayesian setting: at each time step, one job arrives, with parameters drawn from an underlying distribution.
568	Robust Policy Synthesis for Uncertain POMDPs via Convex Optimization	Marnix Suilen, Nils Jansen, Murat Cubuktepe, Ufuk Topcu	We study the problem of policy synthesis for uncertain partially observable Markov decision processes (uPOMDPs).
569	Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints	Steven Carr, Nils Jansen, Ufuk Topcu	By integrating techniques from formal methods and machine learning, we propose an approach to automatically extract a finite-state controller (FSC) from an RNN, which, when composed with a finite-state system model, is amenable to existing formal verification tools.
570	Optimal Planning Modulo Theories	Francesco Leofante, Enrico Giunchiglia, Erika Ábráham, Armando Tacchella	We propose a novel approach that leverages Optimization Modulo Theories (OMT) solvers to implement a domain-independent optimal theory-planner.
571	Sparse Tree Search Optimality Guarantees in POMDPs with Continuous Observation Spaces	Michael H. Lim, Claire Tomlin, Zachary N. Sunberg	This work offers such a justification, proving that a simplified algorithm, partially observable weighted sparse sampling (POWSS), will estimate Q-values accurately with high probability and can be made to perform arbitrarily near the optimal solution by increasing computational power.
572	Optimising Partial-Order Plans Via Action Reinstantiation	Max Waters, Lin Padgham, Sebastian Sardina	This work investigates the problem of optimising a partial-order plan’s (POP) flexibility through the simultaneous transformation of its action ordering and variable binding constraints.
573	Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning	Silvan Sievers, Florian Pommerening, Thomas Keller, Malte Helmert	In this work, we extend this concept to merge-and-shrink (M&S) abstractions that may use labels that do not directly correspond to operators.
574	Decidability Results in First-Order Epistemic Planning	Andrés Occhipinti Liberman, Rasmus Kræmmer Rendsvig	This paper studies the plan existence problem for FODEL planning, showing that while the problem is generally undecidable, the cases of single-agent planning and multi-agent planning with non-modal preconditions are decidable.
575	Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions	Michael Saint-Guillain, Tiago Stegun Vaquero, Jagriti Agrawal, Steve Chien	We propose a fixed-parameter pseudo-polynomial time algorithm to compute the exact DP-robustness of any PSTN under NextFirst protocol, and apply to various PSTN datasets, including the real case of planetary exploration in the context of the Mars 2020 rover, and propose an original structural analysis.
576	Trading Plan Cost for Timeliness in Situated Temporal Planning	Shahaf Shperberg, Andrew Coles, Erez Karpas, Eyal Shimony, Wheeler Ruml	In this paper, we extend the deliberation scheduling approach to address problems in which plans can differ in their cost.
577	Boundary Extension Features for Width-Based Planning with Simulators on Continuous-State Domains	Florent Teichteil-Königsbuch, Miquel Ramirez, Nir Lipovetzky	This paper proposes agnostic feature mapping mechanisms that define the features online, as exploration progresses and the domain of continuous state variables is revealed.
578	DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs	Yunbo Wang, Bo Liu, Jiajun Wu, Yuke Zhu, Simon S. Du, Li Fei-Fei, Joshua B. Tenenbaum	We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network.
579	Trade the System Efficiency for the Income Equality of Drivers in Rideshare	Yifan Xu, Pan Xu	In this paper, we study the income inequality among rideshare drivers due to discriminative cancellations from riders, and the tradeoff between the income inequality (called fairness objective) with the system efficiency (called profit objective).
580	A Unified Model for the Two-stage Offline-then-Online Resource Allocation	Yifan Xu, Pan Xu, Jianping Pan, Jun Tao	In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework.
581	Multi-Robot Adversarial Patrolling Strategies via Lattice Paths	Jan Buermann, Jie Zhang	While the Markov decision process has been the dominant methodology in computing the penetration detection probabilities, we apply enumerative combinatorics to characterise the penetration detection probabilities.
582	Crowd-Steer: Realtime Smooth and Collision-Free Robot Navigation in Densely Crowded Scenarios Trained using High-Fidelity Simulation	Jing Liang, Utsav Patel, Adarsh Jagan Sathyamoorthy, Dinesh Manocha	We present a novel high fidelity 3-D simulator that significantly reduces the sim-to-real gap for collision avoidance in dense crowds using Deep Reinforcement Learning (DRL).
583	Euclidean Pathfinding with Compressed Path Databases	Bojie Shen, Muhammad Aamir Cheema, Daniel Harabor, Peter J. Stuckey	We consider optimal and anytime algorithms for the Euclidean Shortest Path Problem (ESPP) in two dimensions.
584	Lifted Hybrid Variational Inference	Yuqiao Chen, Yibo Yang, Sriraam Natarajan, Nicholas Ruozzi	We investigate two approximate lifted variational approaches that apply to domains with general hybrid potentials, and are expressive enough to capture multi-modality.
585	Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis	Niels Grüttemeier, Christian Komusiewicz	We study the problem of learning the structure of an optimal Bayesian network when additional structural constraints are posed on the network or on its moralized graph.
586	Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry	Timothy van Bremen, Ondrej Kuzelka	We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count of a sentence given an unweighted first-order model counting oracle.
587	Efficient and Robust High-Dimensional Linear Contextual Bandits	Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian	Since large-scale data sets become more and more common, we study the linear contextual bandits in high-dimensional situations.
588	Scaling Up AND/OR Abstraction Sampling	Kalev Kask, Bobak Pezeshki, Filjor Broka, Alexander Ihler, Rina Dechter	In this paper, we introduce AOAS, a new Abstraction Sampling scheme on AND/OR search spaces that allow more flexible use of abstractions by circumventing the properness requirement.
589	Neural Belief Reasoner	Haifeng Qian	This paper proposes a new generative model called neural belief reasoner (NBR).
590	A Complete Characterization of Projectivity for Statistical Relational Models	Manfred Jaeger, Oliver Schulte	In this paper we fill this gap: exploiting representation theorems for infinite exchangeable arrays we introduce a class of directed graphical latent variable models that precisely correspond to the class of projective relational models.
591	State Variable Effects in Graphical Event Models	Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao	In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline.
592	A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study	Luca Romeo, Giuseppe Armentano, Antonio Nicolucci, Marco Vespasiani, Giacomo Vespasiani, Emanuele Frontoni	The aim of this study is the proposal of two different MTL procedures, called spatio-temporal lasso (STL-MTL) and spatio-temporal group lasso (STGL-MTL), which encode the spatio-temporal relatedness using a regularization term and a graph-based approach (i.e., encoding the task relatedness using the structure matrix).
593	Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks	Moein Khajehnejad, Ahmad Asgharian Rezaei, Mahmoudreza Babaei, Jessica Hoffmann, Mahdi Jalili, Adrian Weller	We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes.
594	Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model	Junwen Bai, Shufeng Kong, Carla Gomes	We propose a novel framework for multi-label classification, Multivariate Probit Variational AutoEncoder (MPVAE), that effectively learns latent embedding spaces as well as label correlations.
595	Fighting Wildfires under Uncertainty – A Sequential Resource Allocation Approach	Hau Chan, Long Tran-Thanh, Vignesh Viswanathan	In this paper, we look at the problem of wildfires and propose an efficient resource allocation strategy to cope with both dynamically changing environment and uncertainty.
596	Bridging Cross-Tasks Gap for Cognitive Assessment via Fine-Grained Domain Adaptation	Yingwei Zhang, Yiqiang Chen, Hanchao Yu, Zeping Lv, Qing Li, Xiaodong Yang	In this paper, we propose a novel domain adaptation method, namely the Fine-Grained Adaptation Random Forest (FAT), to bridge the cognitive assessment gap when the data distribution is changed.
597	Embedding Conjugate Gradient in Learning Random Walks for Landscape Connectivity Modeling in Conservation	Pramith Devulapalli, Bistra Dilkina, Yexiang Xue	We propose a moment matching strategy that correlates the model’s hitting and commuting times with those observed empirically.
598	Improving Tandem Mass Spectra Analysis with Hierarchical Learning	Zhengcong Fei	In this paper, we propose a hierarchical multi-stage framework, referred as DeepTag, to identify the peptide sequence for each given spectrum.
599	An Exact Single-Agent Task Selection Algorithm for the Crowdsourced Logistics	Chung-Kyun Han, Shih-Fen Cheng	In this paper, we propose a decision-support algorithm to select delivery tasks for a single crowdsourced worker that best fit his/her upcoming route both in terms of additional travel time and the time window requirements at all stops along his/her route, while at the same time satisfies tasks’ delivery time windows.
600	Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction	Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo	To address these two challenges, we propose a Cross-Interaction Hierarchical Attention network model (CHAT) which uncovers the dynamic occurrence patterns of time-stamped urban anomaly data.
601	Harnessing Code Switching to Transcend the Linguistic Barrier	Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Jaime G. Carbonell	In this paper, we provide a systematic approach to sample code mixed documents leveraging a polyglot embedding based method that requires minimal supervision.
602	Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation	Shufeng Kong, Junwen Bai, Jae Hee Lee, Di Chen, Andrew Allyn, Michelle Stuart, Malin Pinsky, Katherine Mills, Carla Gomes	In this paper, we propose a novel deep model for the zero-inflated multi-target regression problem.
603	Forecasting Avian Migration Patterns using a Deep Bidirectional RNN Augmented with an Auxiliary Task	Kehinde Owoeye	In this work, we consider the task of forecasting migration patterns of the popular Turkey Vulture (Cathartes aura) collected with the aid of satellite telemetry for multiple years at a resolution of one hour.
604	Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth	Amulya Yadav, Roopali Singh, Nikolas Siapoutis, Anamika Barman-Adhikari, Yu Liang	This paper presents CORTA, a software agent that designs personalized rehabilitation programs for homeless youth suffering from opioid addiction.
605	Who Am I?: Towards Social Self-Awareness for Intelligent Agents	Budhitama Subagdja, Han Yi Tay, Ah-Hwee Tan	In this paper, a new model of agent is presented with the capacity to represent itself as a distinct individual with identity, a mind of its own, unique experiences, and social lives.
606	Discrete Biorthogonal Wavelet Transform Based Convolutional Neural Network for Atrial Fibrillation Diagnosis from Electrocardiogram	Qingsong Xie, Shikui Tu, Guoxing Wang, Yong Lian, Lei Xu	In this paper, we propose an Discrete Biorthogonal Wavelet Transform (DBWT) Based Convolutional Neural Network (CNN) for AF detection, shortly called DBWT-AFNet.
607	Generating Interpretable Poverty Maps using Object Detection in Satellite Images	Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon	Here we demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to high resolution (30cm) satellite images.
608	Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control	Connor Riley, Pascal van Hentenryck, Enpeng Yuan	This paper proposes an end-to-end approach that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles.
609	PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction	Feng Zhang, Ningxuan Feng, Yani Liu, Cheng Yang, Jidong Zhai, Shuhao Zhang, Bingsheng He, Jiazao Lin, Xiaoyong Du	To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, and weekdays.
610	Multi-View Joint Graph Representation Learning for Urban Region Embedding	Mingyang Zhang, Tong Li, Yong Li, Pan Hui	In this paper, we focus on learning an embedding space from urban data for urban regions.
611	BitcoinHeist: Topological Data Analysis for Ransomware Prediction on the Bitcoin Blockchain	Cuneyt G. Akcora, Yitao Li, Yulia R. Gel, Murat Kantarcioglu	By capitalizing on the recent advances in Topological Data Analysis, we propose a novel efficient and tractable framework to automatically predict new ransomware transactions in a ransomware family, given only limited records of past transactions.
612	Deep Semantic Compliance Advisor for Unstructured Document Compliance Checking	Honglei Guo, Bang An, Zhili Guo, Zhong Su	In the clause-level semantic comparison, an attention-based semantic relatedness detection model is applied to find the most relevant legal clauses.
613	A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis	Lu Bai, Lixin Cui, Yue Wang, Yuhang Jiao, Edwin R. Hancock	In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks.
614	Data-Driven Market-Making via Model-Free Learning	Yueyang Zhong, YeeMan Bergstrom, Amy Ward	To do this, we use a model-free and off-policy method, Q-learning, coupled with state aggregation, to develop a proposed trading strategy that can be implemented using a simple lookup table.
615	Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection	Xia Cai	Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR).
616	Task-Based Learning via Task-Oriented Prediction Network with Applications in Finance	Di Chen, Yada Zhu, Xiaodong Cui, Carla Gomes	We propose the Task-Oriented Prediction Network (TOPNet), an end-to-end learning scheme that automatically integrates task-based evaluation criteria into the learning process via a learnable surrogate loss function, which directly guides the model towards the task-based goal.
617	Risk Guarantee Prediction in Networked-Loans	Dawei Cheng, Xiaoyang Wang, Ying Zhang, Liqing Zhang	Therefore, in this paper, we propose a dynamic graph-based attention neural network for risk guarantee relationship prediction (DGANN).
618	F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification	Xin Liang, Dawei Cheng, Fangzhou Yang, Yifeng Luo, Weining Qian, Aoying Zhou	In this paper, we propose a neural hierarchical multi-label text classification method, namely F-HMTC, for a financial application scenario with massive event category labels.
619	SEBF: A Single-Chain based Extension Model of Blockchain for Fintech	Yimu Ji, Weiheng Gu, Fei Chen, Xiaoying Xiao, Jing Sun, Shangdong Liu, Jing He, Yunyao Li, Kaixiang Zhang, Fen Mei, Fei Wu	In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF).
620	Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem	Weili Chen, Xiongfeng Guo, Zhiguang Chen, Zibin Zheng, Yutong Lu	To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness.
621	FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining	Zhuang Liu, Degen Huang, Kaiyu Huang, Zhuang Li, Jun Zhao	To address this issue, we present FinBERT (BERT for Financial Text Mining) that is a domain specific language model pre-trained on large-scale financial corpora.
622	MAPS: Multi-Agent reinforcement learning-based Portfolio management System.	Jinho Lee, Raehyun Kim, Seok-Won Yi, Jaewoo Kang	In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS).
623	A Two-level Reinforcement Learning Algorithm for Ambiguous Mean-variance Portfolio Selection Problem	Xin Huang, Duan Li	This paper deals with an ambiguous mean-variance portfolio selection problem with a mixture model on the returns of risky assets, where the proportions of different component distributions are assumed to be unknown to the investor, but being constants (in any time instant).
624	IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data	Ruocheng Guo, Jundong Li, Yichuan Li, K. Selçuk Candan, Adrienne Raglin, Huan Liu	In this work, we formulate the two desiderata as a minimax game.
625	Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction	Wei Li, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu, Qi Su	To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix.
626	An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization	Siyu Lin, Peter A. Beling	In this article, we propose an end-to-end adaptive framework for optimal trade execution based on Proximal Policy Optimization (PPO).
627	Multi-scale Two-way Deep Neural Network for Stock Trend Prediction	Guang Liu, Yuzhao Mao, Qi Sun, Hailong Huang, Weiguo Gao, Xuan Li, Jianping Shen, Ruifan Li, Xiaojie Wang	To automatically learn the multi-scale information in stock data, we propose a Multi-scale Two-way Deep Neural Network.
628	RM-CVaR: Regularized Multiple ?-CVaR Portfolio	Kei Nakagawa, Shuhei Noma, Masaya Abe	In order to improve this problem, we propose RM-CVaR: Regularized Multiple ?-CVaR Portfolio.
629	WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series	Michael Poli, Jinkyoo Park, Ilija Ilievski	In this work, we focus on tackling the problem of NDF position length selection by leveraging high-dimensional sequential data consisting of spot rates, technical indicators and expert tenor patterns.
630	Financial Risk Prediction with Multi-Round Q&A Attention Network	Zhen Ye, Yu Qin, Wei Xu	In this paper, we introduced our Multi-Round Q&A Attention Network, which brings into account the dialogue form in the first place.
631	Risk-Averse Trust Region Optimization for Reward-Volatility Reduction	Lorenzo Bisi, Luca Sabbioni, Edoardo Vittori, Matteo Papini, Marcello Restelli	In this paper, we define a novel measure of risk, which we call reward volatility, consisting of the variance of the rewards under the state-occupancy measure.
632	Robust Market Making via Adversarial Reinforcement Learning	Thomas Spooner, Rahul Savani	We show that adversarial reinforcement learning (ARL) can be used to produce market marking agents that are robust to adversarial and adaptively-chosen market conditions.
633	“The Squawk Bot”: Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering	Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos	In this work, we address the important problem of automatically dis- covering a small set of top news articles associated with a given time series.
634	Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain	Naman Goel, Cyril van Schreven, Aris Filos-Ratsikas, Boi Faltings	In this paper, we identify two biggest challenges in building decentralized, trustless and transparent oracles.
635	The Behavioral Sign of Account Theft: Realizing Online Payment Fraud Alert	Cheng WANG	Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection.
636	Financial Thought Experiment: A GAN-based Approach to Vast Robust Portfolio Selection	Chi Seng Pun, Lei Wang, Hoi Ying Wong	In this paper, we present a new architecture of GAN and adapt it to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment).
637	Market Manipulation: An Adversarial Learning Framework for Detection and Evasion	Xintong Wang, Michael P. Wellman	We propose an adversarial learning framework to capture the evolving game between a regulator who develops tools to detect market manipulation and a manipulator who obfuscates actions to evade detection.
638	Two-stage Behavior Cloning for Spoken Dialogue System in Debt Collection	Zihao Wang, Jia Liu, Hengbin Cui, Chunxiang Jin, Minghui Yang, Yafang Wang, Xiaolong Li, Renxin Mao	To solve this problem, we propose the behavior-cloning-based collection robot framework without any dialogue flow configuration, called two-stage behavior cloning (TSBC).
639	Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction	Qianggang Ding, Sifan Wu, Hao Sun, Jiadong Guo, Jian Guo	In this paper, we propose a novel approach based on the Transformer to tackle the stock movement prediction task.
640	Relation-Aware Transformer for Portfolio Policy Learning	Ke Xu, Yifan Zhang, Deheng Ye, Peilin Zhao, Mingkui Tan	In this paper, under a deep reinforcement learning paradigm for portfolio selection, we propose a novel Relation-aware Transformer (RAT) to handle these aspects.
641	Federated Meta-Learning for Fraudulent Credit Card Detection	Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang	In this paper, we introduce a novel framework termed as federated meta-learning for fraud detection.
642	Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining	Shuo Yang, Zhiqiang Zhang, Jun Zhou, Yang Wang, Wang Sun, Xingyu Zhong, Yanming Fang, Quan Yu, Yuan Qi	In this paper, tackling the data deficiency problem of financial risk analysis for SMEs, we propose an innovative financial risk analysis framework with graph-based supply chain mining.
643	A Unified Model for Financial Event Classification, Detection and Summarization	Quanzhi Li, Qiong Zhang	In this paper, we present a unified model for detecting, classifying and summarizing financial events.
644	Interpretable Multimodal Learning for Intelligent Regulation in Online Payment Systems	Shuoyao Wang, Diwei Zhu	Inspired by the attention mechanism in nature language processing, we propose a novel cross-modal and intra-modal attention network (CIAN) to investigate the relation between the text and transaction.
645	Online Portfolio Selection with Cardinality Constraint and Transaction Costs based on Contextual Bandit	Mengying Zhu, Xiaolin Zheng, Yan Wang, Qianqiao Liang, Wenfang Zhang	In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints.
646	Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract)	Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi	In this paper, we propose and apply an offline Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical policy that makes decisions at both problem and step levels.
647	VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract)	Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis	First, we describe a language for representing individual player actions on the pitch. This language unifies several existing formats which greatly simplifies automated analysis and this language is becoming widely used in the soccer analytics community. Second, we describe our framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened.
648	Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification (Extended Abstract)	Zhenpeng Chen, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, Xuanzhe Liu	In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns.
649	Methodological Issues in Recommender Systems Research (Extended Abstract)	Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach	In this paper, we discuss these observations in detail, and reflect on the related fundamental problem of over-reliance on offline experiments in recommender systems research.
650	Playing Atari with Six Neurons (Extended Abstract)	Giuseppe Cuccu, Julian Togelius, Philippe Cudré-Mauroux	To this end, we propose a new method for learning policies and compact state representations separately but simultaneously for policy approximation in reinforcement learning.
651	A Formal Approach for Cautious Reasoning in Answer Set Programming (Extended Abstract)	Giovanni Amendola, Carmine Dodaro, Marco Maratea	In this paper we deal with cautious reasoning tasks in ASP, and design, implement and test novel abstract solutions, borrowed from backbone computation in SAT.
652	On the Splitting Property for Epistemic Logic Programs (Extended Abstract)	Pedro Cabalar, Jorge Fandinno, Luis Fariñas del Cerro	In this paper, we review an extension of the well-known splitting property for logic programs to the epistemic case.
653	Statistical Learning with a Nuisance Component (Extended Abstract)	Dylan J. Foster, Vasilis Syrgkanis	We provide excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate a target parameter depends on an unknown parameter that must be estimated from data (a "nuisance parameter").
654	Learning URI Selection Criteria to Improve the Crawling of Linked Open Data (Extended Abstract)	Hai Huang, Fabien Gandon	In this paper, we propose and compare approaches to learn strategies for crawling Linked Data on the Web by predicting whether a newly discovered URI will lead to an RDF data source or not.
655	Human Values and Digital Patterns in Physical Exercise (Extended Abstract)	Yelena Mejova, Kyriaki Kalimeri	In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion.
656	Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)	Eoin M. Kenny, Elodie Ruelle, Anne Geoghegan, Laurence Shalloo, Micheál O’Leary, Michael O’Donovan, Mohammed Temraz, Mark T. Keane	This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption.
657	Specializing Word Embeddings (for Parsing) by Information Bottleneck (Extended Abstract)	Xiang Lisa Li, Jason Eisner	We propose a very fast variational information bottleneck (VIB) method to nonlinearly compress these embeddings, keeping only the information that helps a discriminative parser.
658	NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm (Extended Abstract)	Zhichao Lu, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, Vishnu Naresh Boddeti	This paper introduces NSGA-Net — an evolutionary search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures, and finally an exploitation step that utilizes the hidden useful knowledge stored in the entire history of evaluated neural architectures in the form of a Bayesian Network.
659	Supporting Historical Photo Identification with Face Recognition and Crowdsourced Human Expertise (Extended Abstract)	Vikram Mohanty, David Thames, Sneha Mehta, Kurt Luther	In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861-65).
660	Commonsense Reasoning to Guide Deep Learning for Scene Understanding (Extended Abstract)	Mohan Sridharan, Tiago Mota	Our architecture uses non-monotonic logical reasoning with incomplete commonsense domain knowledge, and incremental inductive learning, to guide the construction of deep network models from a small number of training examples.
661	Learning Optimal Decision Trees using Constraint Programming (Extended Abstract)	Hélène Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, Pierre Schaus	In this paper, we introduce a new approach to learn decision trees using constraint programming.
662	Lagrangian Decomposition for Classical Planning (Extended Abstract)	Florian Pommerening, Gabriele Röger, Malte Helmert, Hadrien Cambazad, Louis-Martin Rousseau, Domenico Salvagnin	We analyze the application of Lagrangian decomposition, a classical tool in mathematical programming, to cost partitioning of operator-counting heuristics.
663	Bidirectional Heuristic Search: Expanding Nodes by a Lower Bound	Shahaf Shperberg, Ariel Felner, Nathan Sturtevant, Eyal Shimony, Avi Hayoun	Building on these results, we introduce DVCBS, a new algorithm that aims to to further reduce the number of expansions.
664	Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images (Extended Abstract)	Kuniyuki Takahashi, Jethro Tan	We, therefore, propose a model to estimate the degree of tactile properties from visual perception alone (e.g., the level of slipperiness or roughness).
665	A User Interface for Exploring and Querying Knowledge Graphs (Extended Abstract)	Hernán Vargas, Carlos Buil-Aranda, Aidan Hogan, Claudia López	In this extended abstract, we provide a summary of our work on a language and visual interface — called RDF Explorer — that help non-expert users to navigate and query knowledge graphs.
666	Bridging the Gap between Training and Inference for Neural Machine Translation (Extended Abstract)	Wen Zhang, Yang Feng, Qun Liu	In this paper, we address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence during training.
667	Survey on Feature Transformation Techniques for Data Streams	Maroua Bahri, Albert Bifet, Silviu Maniu, Heitor Murilo Gomes	In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.
668	The Emerging Landscape of Explainable Automated Planning & Decision Making	Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati	In this paper, we provide a comprehensive outline of the different threads of work in Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years and contrast that with earlier efforts in the field in terms of techniques, target users, and delivery mechanisms.
669	Explanation Perspectives from the Cognitive Sciences—A Survey	Ramya Srinivasan, Ajay Chander	Towards this end, we survey papers from the cognitive science literature that address the following broad questions: (1) what is an explanation, (2) what are explanations for, and 3) what are the characteristics of good and bad explanations.
670	Automatic Curriculum Learning For Deep RL: A Short Survey	Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, Pierre-Yves Oudeyer	The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.
671	A Survey on Computational Propaganda Detection	Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, Preslav Nakov	In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities.
672	Turning 30: New Ideas in Inductive Logic Programming	Andrew Cropper, Sebastijan Duman?i?, Stephen H. Muggleton	We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to learning background knowledge, and the use of different technologies, notably answer set programming and neural networks.
673	The Blind Men and the Elephant: Integrated Offline/Online Optimization Under Uncertainty	Allegra De Filippo, Michele Lombardi, Michela Milano	In this survey we highlight the potential shortcomings of pure methods when applied to mixed offline/online problems, we review the strategies that have been designed to take advantage of this integration, and we suggest directions for future research.
674	Goal Recognition Design – Survey	Sarah Keren, Avigdor Gal, Erez Karpas	This survey presents the solutions developed for evaluation and optimization in the GRD context, a discussion on the use of GRD in a variety of real-world applications, and suggestions of possible future avenues of GRD research.
675	From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information	Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan	In this paper, we focus on the survey of these new summarization tasks and approaches in the real-world application.
676	Heterogeneous Network Representation Learning	Yuxiao Dong, Ziniu Hu, Kuansan Wang, Yizhou Sun, Jie Tang	In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations.
677	A Brief History of Learning Symbolic Higher-Level Representations from Data (And a Curious Look Forward)	Stefan Kramer	In the paper, I will give a survey of various approaches to learning symbolic higher-level representations: feature construction and constructive induction, predicate invention, propositionalization, pattern mining, and mining time series patterns.
678	Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective	Luís C. Lamb, Artur d’Avila Garcez, Marco Gori, Marcelo O.R. Prates, Pedro H.C. Avelar, Moshe Y. Vardi	In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing.
679	Collective Decision Making under Incomplete Knowledge: Possible and Necessary Solutions	Jérôme Lang	I give here a survey of works along this line.
680	Planning Algorithms for Zero-Sum Games with Exponential Action Spaces: A Unifying Perspective	Levi H. S. Lelis	In this paper we review several planning algorithms developed for zero-sum games with exponential action spaces, i.e., spaces that grow exponentially with the number of game components that can act simultaneously at a given game state.
681	Reasoning About Inconsistent Formulas	Joao Marques-Silva, Carlos Mencía	This paper overviews approaches for analyzing inconsistent formulas, focusing on finding and enumerating explanations of and corrections for inconsistency, but also on solving optimization problems modeled as inconsistent formulas.
682	A Survey on Using Gaze Behaviour for Natural Language Processing	Sandeep Mathias, Diptesh Kanojia, Abhijit Mishra, Pushpak Bhattacharya	In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time.
683	BDI Agent Architectures: A Survey	Lavindra de Silva, Felipe Meneguzzi, Brian Logan	In this paper, we survey the main approaches to each component of the BDI architecture, how these have been realised in agent programming languages, and discuss the trade-offs inherent in each approach.
684	Recent Developments in Boolean Matrix Factorization	Pauli Miettinen, Stefan Neumann	In this survey, we give a concise summary of the efforts of all of these communities and raise some open questions which in our opinion require further investigation.
685	Incorporating Extra Knowledge to Enhance Word Embedding	Arpita Roy, Shimei Pan	In this survey, we summarize the recent advances in incorporating extra knowledge to enhance word embedding.
686	The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation	Tommaso Pasini	In this work, we first introduce the reader to the task of WSD through a short historical digression and then take the stock of the advancements to alleviate the knowledge acquisition bottleneck problem.
687	From Statistical Relational to Neuro-Symbolic Artificial Intelligence	Luc de Raedt, Sebastijan Duman?i?, Robin Manhaeve, Giuseppe Marra	This survey identifies several parallels across seven different dimensions between these two fields.
688	Human Gaze Assisted Artificial Intelligence: A Review	Ruohan Zhang, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana Ballard, Mary Hayhoe	We provide a high-level overview of the research efforts in these fields, including collecting human gaze data sets, modeling gaze behaviors, and utilizing gaze information in various applications, with the goal of enhancing communication between these research areas.
689	Pure-Past Linear Temporal and Dynamic Logic on Finite Traces	Giuseppe De Giacomo, Antonio Di Stasio, Francesco Fuggitti, Sasha Rubin	We review PLTLf and PLDLf, the pure-past versions of the well-known logics on finite traces LTLf and LDLf, respectively.
690	Fair Division: The Computer Scientist’s Perspective	Toby Walsh	I survey recent progress on a classic and challenging problem in social choice: the fair division of indivisible items.
691	Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification	Zheng Wang, Zhixiang Wang, Yinqiang Zheng, Yang Wu, Wenjun Zeng, Shin’ichi Satoh	In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies.
692	Deep Learning for Community Detection: Progress, Challenges and Opportunities	Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, Philip S Yu	Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.
693	Learning for Graph Matching and Related Combinatorial Optimization Problems	Junchi Yan, Shuang Yang, Edwin Hancock	This survey gives a selective review of recent development of machine learning (ML) for combinatorial optimization (CO), especially for graph matching.
694	A Survey on Representation Learning for User Modeling	Sheng Li, Handong Zhao	This paper presents a comprehensive survey of recent advances in user modeling from the perspective of representation learning.
695	Forgetting Auxiliary Atoms in Forks (Extended Abstract)	Felicidad Aguado, Pedro Cabalar, Jorge Fandinno, David Pearce, Gilberto Pérez, Concepción Vidal	In this paper, we introduce a conservative extension of Equilibrium Logic and its monotonic basis, the logic of Here-and-There, in which we deal with a new connective we call fork.
696	Xeggora: Exploiting Immune-to-Evidence Symmetries with Full Aggregation in Statistical Relational Models (Extended Abstract)	Mohammad Mahdi Amirian, Saeed Shiry Ghidary	We propose Full-Constraint-Aggregation, a superior algorithm to CPA which exploits the ignored symmetries via a lifted translation method and some constraint relaxations.
697	Analogy Between Concepts (Extended Abstract)	Nelly Barbot, Laurent Miclet, Henri Prade	In this paper, we more particularly consider “relational proportions” of the form “object A has the same relationship with attribute a as object B with attribute b”.
698	Predicting Strategic Behavior from Free Text (Extended Abstract)	Omer Ben-Porat, Lital Kuchy, Sharon Hirsch, Guy Elad, Roi Reichart, Moshe Tennenholtz	This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web.
699	Rational Closure For All Description Logics (Extended Abstract)	Piero A. Bonatti	In this paper we prove that RC cannot be extended to logics that do not satisfy the disjoint model union property, including SROIQ(D).
700	Automated Construction of Bounded-Loss Imperfect-Recall Abstractions in Extensive-Form Games (Extended Abstract)	Ji?í ?ermák, Viliam Lisý, Branislav Bošanský	We present novel domain-independent abstraction methods for creating very coarse abstractions of EFGs that still compute strategies that are (near) optimal in the original game.
701	Variable Elimination in Binary CSPs (Extended Abstract)	Martin C. Cooper, Achref El Mouelhi, Cyril Terrioux	We investigate rules which allow variable elimination in binary CSP (constraint satisfaction problem) instances while conserving satisfiability.
702	Determining Inference Semantics for Disjunctive Logic Programs (Extended Abstract)	Yi-Dong Shen, Thomas Eiter	To address this, we present a novel and more permissive semantics, called determining inference semantics.
703	Algorithms for Estimating the Partition Function of Restricted Boltzmann Machines (Extended Abstract)	Oswin Krause, Asja Fischer, Christian Igel	We devised a unifying theoretical framework for algorithms for estimating the partition function, including Annealed Importance Sampling (AIS) and Bennett’s Acceptance Ratio method (BAR).
704	Variational Bayes in Private Settings (VIPS) (Extended Abstract)	James R. Foulds, Mijung Park, Kamalika Chaudhuri, Max Welling	We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method.
705	On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)	Vincent Francois-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau	In the context of reinforcement learning with partial observability, this paper provides an analysis of the tradeoff between these two error sources.
706	Ontology Reasoning with Deep Neural Networks (Extended Abstract)	Patrick Hohenecker, Thomas Lukasiewicz	In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology reasoning.
707	Compositionality Decomposed: How do Neural Networks Generalise? (Extended Abstract)	Dieuwke Hupkes, Verna Dankers, Mathijs Mul, Elia Bruni	As a response to this controversy, we present a set of tests that provide a bridge between, on the one hand, the vast amount of linguistic and philosophical theory about compositionality of language and, on the other, the successful neural models of language.
708	Story Embedding: Learning Distributed Representations of Stories based on Character Networks (Extended Abstract)	O-Joun Lee, Jason J. Jung	This study aims to represent stories in narrative works (i.e., creative works that contain stories) with a fixed-length vector.
709	Proving Semantic Properties as First-Order Satisfiability (Extended Abstract)	Salvador Lucas	In this paper we show how to prove such semantic properties ? of Th by just finding a model A of Th?{?}?Z?, where Z? is an appropriate (possibly empty) theory depending on ? only.
710	Point at the Triple: Generation of Text Summaries from Knowledge Base Triples (Extended Abstract)	Pavlos Vougiouklis, Eddy Maddalena, Jonathon Hare, Elena Simperl	We investigate the problem of generating natural language summaries from knowledge base triples.
711	A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract)	Artuur Leeuwenberg, Marie-Francine Moens	This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.
712	Formulas Free From Inconsistency: An Atom-Centric Characterization in Priest’s Minimally Inconsistent LP (Extended Abstract)	Kedian Mu	In this paper, we propose a notion of Bi-free formula to describe formulas that are free from inconsistency in both syntactic characterization and paraconsistent models in the framework of Priest’s minimally inconsistent LP.
713	Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract)	Alvaro Perez Diaz, Enrico H. Gerding, Frank McGroarty	We propose a novel decentralised bidding coordination algorithm based on the Alternating Direction Method of Multipliers (ADMM).
714	From Support Propagation to Belief Propagation in Constraint Programming (Extended Abstract)	Gilles Pesant	We investigate a richer propagation medium for CP made possible by recent work on counting solutions inside constraints.
715	The Computational Complexity of Angry Birds (Extended Abstract)	Matthew Stephenson, Jochen Renz, Xiaoyu Ge	In this paper we present several proofs for the computational complexity of the physics-based video game Angry Birds.
716	Language Independent Sequence Labelling for Opinion Target Extraction (Extended Abstract)	Rodrigo Agerri, German Rigau	In this paper we present a language independent system to model Opinion Target Extraction (OTE) as a sequence labelling task.
717	Context Vectors Are Reflections of Word Vectors in Half the Dimensions (Extended Abstract)	Zhenisbek Assylbekov, Rustem Takhanov	This paper takes a step towards the theoretical analysis of the relationship between word embeddings and context embeddings in models such as word2vec.
718	Knowing-How under Uncertainty (Extended Abstract)	Pavel Naumov, Jia Tao	This article combines these two research lines by introducing a bimodal logic containing knowledge and know-how modalities, both labeled with a degree of uncertainty.
719	Swarm Intelligence for Self-Organized Clustering (Extended Abstract)	Michael C. Thrun, Alfred Ultsch	Swarm Intelligence for Self-Organized Clustering (Extended Abstract)
720	Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation (Extended Abstract)	Eric Timmons, Brian C. Williams	In this paper we present an approach (A*BC) that unifies best-first enumeration and conflict-directed search in relatively unconstrained problems through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space.
721	OptStream: Releasing Time Series Privately (Extended Abstract)	Ferdinando Fioretto, Pascal Van Hentenryck	Motivated by an application in energy systems, this paper presents OptStream, a novel algorithm for releasing differentially private data streams under the w-event model of privacy.
722	Incentivizing Evaluation with Peer Prediction and Limited Access to Ground Truth (Extended Abstract)	Alice Gao, James Wright, Kevin Leyton-Brown	We show that this problem is unavoidable whenever agents are able to coordinate using low-cost signals about the items being evaluated (e.g., text labels or pictures). We then consider ways of circumventing this problem by comparing agents’ reports to ground truth, which is available in practice when there exist trusted evaluators—such as teaching assistants in the peer grading scenario—who can perform a limited number of unbiased (but noisy) evaluations.
723	IKBT: Solving Symbolic Inverse Kinematics with Behavior Tree (Extended Abstract)	Dianmu Zhang, Blake Hannaford	We developed IKBT, a knowledge-based intelligent system that can mimic human experts’ behaviors in solving closed-from inverse kinematics using Behavior Tree.
724	Bridging Causality and Learning: How Do They Benefit from Each Other?	Mingming Gong	In this paper, I will be focusing on two essen- tial problems that bridge causality and learning and investigate how they can benefit from each other.
725	Towards Trustable Explainable AI	Alexey Ignatiev	This paper overviews the advances of the rigorous logic-based approach to XAI and argues that it is indispensable if trustable XAI is of concern.
726	Developing an Integrated Model of Speech Entrainment	Rivka Levitan	A more integrated model of entrainment is proposed, which looks for consistent explanations of entrainment behavior on specific features and how they interact with speaker, session, and utterance characteristics.
727	Optimization Learning: Perspective, Method, and Applications	Risheng Liu	We move beyond these limits and propose a theoretically guaranteed optimization learning paradigm, a generic and provable paradigm for nonconvex inverse problems, and develop a series of convergent deep models.
728	Closing the Loop: Bringing Humans into Empirical Computational Social Choice and Preference Reasoning	Nicholas Mattei	I argue that of equal importance to the theoretical results are impacts in research and development from the empirical part of the computer scientists toolkit: data, system building, and human interaction.
729	Mechanism Design with Uncertainty	Taiki Todo	My research is summarized as mechanism design with uncertainty.
730	Online Learning in Changing Environments	Lijun Zhang	Specifically, we have proposed novel algorithms to minimize the dynamic regret and adaptive regret, and investigated the relationship between them.
731	Transparent Intent for Explainable Shared Control in Assistive Robotics	Mark Zolotas, Yiannis Demiris	We situate this problem in our Explainable Shared Control paradigm and present ongoing efforts to achieve transparency in human-robot collaboration.
732	Towards High-Level Intrinsic Exploration in Reinforcement Learning	Nicolas Bougie, Ryutaro Ichise	We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning.
733	An Improved Latent Low Rank Representation for Automatic Subspace Clustering	Ya-nan Han, Jian-wei Liu, Xiong-lin Luo	In this paper, we proposed an improved latent LRR model with a distance regularization and a non-negative regularization jointly, which can effectively discover the global and local structure of data for graph learning and improve the expression of the model.
734	Learning Sparse Neural Networks for Better Generalization	Shiwei Liu	To tackle these problems, this Ph.D. research aims to study the generalization of sparse neural networks, and to propose more efficient approaches that can yield sparse neural networks with generalization bounds.
735	Population Location and Movement Estimation through Cross-domain Data Analysis	Xinghao Yang, Wei Liu	In this paper, we propose to utilize multi-domain statistical data to estimate people movements under the assumption that most population tend to move to areas with similar or better living conditions.
736	Design Adaptive AI for RTS Game by Learning Player’s Build Order	Guillaume Lorthioir, Katsumi Inoue	In this paper, a method using simulation and bayesian programming to infer the player’s strategy in a Real-Time-Strategy game (RTS) is described, as well as how we could use it to make more adaptive AI for this kind of game and thus make more challenging and entertaining games for the players.
737	Strategies for Cooperative UAVs Using Model Predictive Control	Amith Manoharan	In this work, we present the use of nonlinear model predictive control (NMPC) for two different applications involving cooperative UAVs.
738	Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network	Bonaventure C. Molokwu, Ziad Kobti	Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN).
739	On Building an Interpretable Topic Modeling Approach for the Urdu Language	Zarmeen Nasim	Using semanti-cally rich feature extraction approaches including Word2Vec and BERT, the proposed approach, in the next step, would experiment with different clus-tering and topic modeling techniques to produce a list of potential topics for a given set of documents.
740	Spatio-Temporal Change Detection Using Granger Sequence Pattern	Nat Pavasant, Masayuki Numao, Ken-ichi Fukui	This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events.
741	Generating Natural Counterfactual Visual Explanations	Wenqi Zhao, Satoshi Oyama, Masahito Kurihara	Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text.
742	Towards an Artificial Argumentation System	Ramon Ruiz-Dolz	In this work, some of our initial contributions and the foundations of this research field are presented.
743	Context Aware Sequence Modeling	Kyungwoo Song	In this paper, we present our research works on context modeling and its dynamics modeling over time.
744	Predictive Uncertainty Estimation for Tractable Deep Probabilistic Models	Julissa Villanueva Llerena	In this work, we will develop efficient estimators of the predictive uncertainty that are robust to data scarcity and outliers.
745	End-to-End Signal Factorization for Speech: Identity, Content, and Style	Jennifer Williams	Based on these findings, a new technique is proposed to factorize multiple types of information from the speech signal simultaneously using a combination of state-of-the-art machine learning methods for speech processing.
746	Beyond Labels: Knowledge Elicitation using Deep Metric Learning and Psychometric Testing	Lu YIn	Here, we provide a method for efficient hierarchical knowledge elicitation (HKE) from experts working with high-dimensional data such as images or videos.
747	Generalized Representation Learning Methods for Deep Reinforcement Learning	Hanhua Zhu	In this work, I propose generalized representation learning methods to obtain compact state space suitable for RL from a raw observation state.
748	GenC: A Fast Tool for Applications Involving Belief Revision	Aaron Hunter, John Agapeyev	In this demonstration paper, we describe GenC, a tool that is able to quickly calculate the result of AGM belief revision for formulas with hundreds of variables and millions of clauses.
749	PyDL8.5: a Library for Learning Optimal Decision Trees	Gaël Aglin, Siegfried Nijssen, Pierre Schaus	We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs).
750	Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions	Valentijn Borghuis, Luca Angioloni, Lorenzo Brusci, Paolo Frasconi	We demonstrate a pattern-based MIDI music generation system with a generation strategy based on Wasserstein autoencoders and a novel variant of pianoroll descriptions of patterns which employs separate channels for note velocities and note durations and can be fed into classic DCGAN-style convolutional architectures.
751	An Anomaly Detection and Explainability Framework using Convolutional Autoencoders for Data Storage Systems	Roy Assaf, Ioana Giurgiu, Jonas Pfefferle, Serge Monney, Haris Pozidis, Anika Schumann	We demonstrate the application of this framework along with an intuitive interactive web interface which was developed for data storage system anomaly detection.
752	DeepVentilation: Learning to Predict Physical Effort from Breathing	Sagar Sen, Pierre Bernabé, Erik Johannes B.L.G. Husom	In this demo, we present DeepVentilation, a deep learning system to predict minute ventilation in litres of air a person moves in one minute uniquely from real-time measurement of rib-cage breathing forces.
753	Yolo4Apnea: Real-time Detection of Obstructive Sleep Apnea	Sondre Hamnvik, Pierre Bernabé, Sagar Sen	In this demo, we present Yolo4Apnea a deep learning system extending You Only Look Once (Yolo) system to detect sleep apnea events from abdominal breathing patterns in real-time enabling immediate awareness and action.
754	Efficient and Modularized Training on FPGA for Real-time Applications	Shreyas Kolala Venkataramanaiah, Xiaocong Du, Zheng Li, Shihui Yin, Yu Cao, Jae-sun Seo	We demonstrate the performance of representative CNNs trained for CIFAR-10 on Intel Stratix-10 MX FPGA, evaluating both the conventional training procedure and the online learning algorithm.
755	TouIST: a Friendly Language for Propositional Logic and More	Jorge Fernandez, Olivier Gasquet, Andreas Herzig, Dominique Longin, Emiliano Lorini, Frédéric Maris, Pierre Régnier	We present the automatic translator TouIST that provides a simple language to generate logical formulas from a problem description.
756	Ddo, a Generic and Efficient Framework for MDD-Based Optimization	Xavier Gillard, Pierre Schaus, Vianney Coppé	This paper presents ddo, a generic and efficient library to solve constraint optimization problems with decision diagrams.
757	How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines	Johannes Huegle, Christopher Hagedorn, Matthias Uflacker	In this demo, we showcase how a CGM of the production process is incorporated into a monitoring tool to function as a decision-support system for an operator of a modern automotive body shop assembly line and enables fast and effective handling of failures and quality deviations.
758	Certifai: A Toolkit for Building Trust in AI Systems	Jette Henderson, Shubham Sharma, Alan Gee, Valeri Alexiev, Steve Draper, Carlos Marin, Yessel Hinojosa, Christine Draper, Michael Perng, Luis Aguirre, Michael Li, Sara Rouhani, Shorya Consul, Susan Michalski, Akarsh Prasad, Mayank Chutani, Aditya Kumar, Shahzad Alam, Prajna Kandarpa, Binnu Jesudasan, Colton Lee, Michael Criscolo, Sinead Williamson, Matt Sanchez, Joydeep Ghosh	In this paper, we provide a brief overview of a demonstration of Cortex Certifai’s capabilities.
759	BlueMemo: Depression Analysis through Twitter Posts	Pengwei Hu, Chenhao Lin, Hui Su, Shaochun Li, Xue Han, Yuan Zhang, Jing Mei	To address this problem, we proposed a new system for social media screening of depressed patients named BlueMemo.
760	AutoSurvey: Automatic Survey Generation based on a Research Draft	Hen-Hsen Huang	This work presents AutoSurvey, an intelligent system that performs literature survey and generates a summary specific to a research draft.
761	AILA: A Question Answering System in the Legal Domain	Weiyi Huang, Jiahao Jiang, Qiang Qu, Min Yang	In this paper, we develop an Artificial Intelligence Law Assistant (AILA) for question answering in the domain of Chinese laws.
762	An Interactive Visualization Platform for Deep Symbolic Regression	Joanne T. Kim, Sookyung Kim, Brenden K. Petersen	Thus, we present an interactive symbolic regression framework that allows users not only to configure runs, but also to control the system during training.
763	RLCard: A Platform for Reinforcement Learning in Card Games	Daochen Zha, Kwei-Herng Lai, Songyi Huang, Yuanpu Cao, Keerthana Reddy, Juan Vargas, Alex Nguyen, Ruzhe Wei, Junyu Guo, Xia Hu	We present RLCard, a Python platform for reinforcement learning research and development in card games.
764	An AI-empowered Visual Storyline Generator	Chang Liu, Zhao Yong Lim, Han Yu, Zhiqi Shen, Ian Dixon, Zhanning Gao, Pan Wang, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao	This paper outlines Visual Storyline Generator (VSG), an artificial intelligence (AI)-empowered system that automatically generates visual storylines based on a set of images and video footages provided by the user.
765	Keep It Real: a Window to Real Reality in Virtual Reality	Baihan Lin	This paper proposed a new interaction paradigm in the virtual reality (VR) environments, which consists of a virtual mirror or window projected onto a virtual surface, representing the correct perspective geometry of a mirror or window reflecting the real world.
766	FlowSynth: Simplifying Complex Audio Generation Through Explorable Latent Spaces with Normalizing Flows	Philippe Esling, Naotake Masuda, Axel Chemla–Romeu-Santos	Here, we discuss the details of integrating these high-level features to develop new interaction schemes between a human user and the generating device: parameters inference from audio, high-level preset visualization and interpolation, that can be used both in off-time and real-time situations.
767	Putting Accountability of AI Systems into Practice	Beatriz San Miguel, Aisha Naseer, Hiroya Inakoshi	To put accountability into practice, this paper presents the Global-view Accountability Framework (GAF) that considers auditability and redress of conflicting information arising from a context with two or more AI systems which can produce a negative impact.
768	A Multi-player Game for Studying Federated Learning Incentive Schemes	Kang Loon Ng, Zichen Chen, Zelei Liu, Han Yu, Yang Liu, Qiang Yang	This paper proposes FedGame, a multi-player game to study how FL participants make action selection decisions under different incentive schemes.
769	Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health	William Ogallo, Skyler Speakman, Victor Akinwande, Kush R Varshney, Aisha Walcott-Bryant, Charity Wayua, Komminist Weldemariam	In this work, we demonstrate how a tool for inspecting "black box" machine learning models can be used to generate actionable insights from models trained on demographic health survey data to predict neonatal mortality.
770	ProbAnch: a Modular Probabilistic Anchoring Framework	Andreas Persson, Pedro Zuidberg Dos Martires, Luc de Raedt, Amy Loufti	In this paper, we present ProbAnch, a modular data-driven anchoring framework, whose implementation requires a variety of well-orchestrated components, including a probabilistic reasoning system.
771	Decision Platform for Pattern Discovery and Causal Effect Estimation in Contraceptive Discontinuation	Celia Cintas, Ramya Raghavendra, Victor Akinwande, Aisha Walcott-Bryant, Charity Wayua, Komminist Weldemariam	We introduce an AI-based decision platform capable of analyzing event data to identify patterns of contraceptive uptake that are unique to a subpopulation of interest.
772	Lossless Semantic Round-Tripping in PENG ASP	Rolf Schwitter	We demonstrate that the PENG ASP system can be used to bridge the gap between a (seemingly) informal specification and an executable answer set program.
773	A Testbed for Studying COVID-19 Spreading in Ride-Sharing Systems	Harrison Jun Yong Wong, Zichao Deng, Han Yu, Jianqiang Huang, Cyril Leung, Chunyan Miao	In this paper, we present a multi-agent testbed to study the spread of infectious diseases through such a system.
774	A Gamified Assessment Platform for Predicting the Risk of Dementia +Parkinson’s disease (DPD) Co-Morbidity	Zhiwei Zeng, Hongchao Jiang, Yanci Zhang, Zhiqi Shen, Jun Ji, Martin J. Mckeown, Jing Jih Chin, Cyril Leung, Chunyan Miao	In this paper, we demonstrate a novel AI-powered solution to provide early detection of the onset of Dementia + Parkinson’s disease (DPD) co-morbidity, a condition which severely limits a senior’s ability to live actively and independently.
775	SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis	Xi Chen, Hao Zhai, Danqian Liu, Weifu Li, Chaoyue Ding, Qiwei Xie, Hua Han	Therefore, we develop an AI-based multi-species tracking and segmentation system, SiamBOMB, for real-time and automatic home-cage animal behavioral analysis.
776	A Speech-to-Knowledge-Graph Construction System	Xiaoyi Fu, Jie Zhang, Hao Yu, Jiachen Li, Dong Chen, Jie Yuan, Xindong Wu	This paper presents a HAO-Graph system that generates and visualizes knowledge graphs from a speech in real-time.
777	Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization	Wei Niu, Pu Zhao, Zheng Zhan, Xue Lin, Yanzhi Wang, Bin Ren	To address this problem, we propose a set of hardware-friendly structured model pruning and compiler optimization techniques to accelerate DNN executions on mobile devices.
778	AI-Powered Oracle Bone Inscriptions Recognition and Fragments Rejoining	Chongsheng Zhang, Ruixing Zong, Shuang Cao, Yi Men, Bofeng Mo	In this paper, we introduce our contributions in AI-Powered Oracle Bone (OB) fragments rejoining and OBI recognition.