Paper Digest: AAAI 2017 Highlights

February 3, 2017October 8, 2019 admin

The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2017, it is to be held in San Francisco, California.

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

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

Paper Digest Team
team@paperdigest.org

TABLE 1: AAAI 2017 Papers

	Title	Authors	Highlight
1	SnapNETS: Automatic Segmentation of Network Sequences with Node Labels	Sorour E. Amiri, Liangzhe Chen, B. Aditya Prakash	In this paper, we study the problem of segmenting graph sequences with labeled nodes.
2	Taming the Matthew Effect in Online Markets with Social Influence	Franco Berbeglia, Pascal Van Hentenryck	Taming the Matthew Effect in Online Markets with Social Influence
3	A Leukocyte Detection Technique in Blood Smear Images Using Plant Growth Simulation Algorithm	Deblina Bhattacharjee, Anand Paul	Therefore, in order to automate and optimize the process, the nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied in this paper.
4	Partitioned Sampling of Public Opinions Based on Their Social Dynamics	Weiran Huang, Liang Li, Wei Chen	In this paper, we explore the idea of partitioned sampling, which partitions individuals with high opinion similarities into groups and then samples every group separately to obtain an accurate estimate of the population opinion.
5	Novel Geometric Approach for Global Alignment of PPI Networks	Yangwei Liu, Hu Ding, Danyang Chen, Jinhui Xu	In this paper we present a novel geometric method for the problem of global pairwise alignment of protein-protein interaction (PPI) networks.
6	Towards Better Understanding the Clothing Fashion Styles: A Multimodal Deep Learning Approach	Yihui Ma, Jia Jia, Suping Zhou, Jingtian Fu, Yejun Liu, Zijian Tong	In this paper, we aim to better understand the clothing fashion styles.
7	Profit-Driven Team Grouping in Social Networks	Shaojie Tang	In this paper, we investigate the profit-driven team grouping problem in social networks.
8	Gated Neural Networks for Option Pricing: Rationality by Design	Yongxin Yang, Yu Zheng, Timothy M. Hospedales	We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable.
9	Local Discriminant Hyperalignment for Multi-Subject fMRI Data Alignment	Muhammad Yousefnezhad, Daoqiang Zhang	By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant Hyperalignment (LDHA) as a novel supervised HA method, which can provide better functional alignment for MVP analysis.
10	Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images	Lequan Yu, Xin Yang, Hao Chen, Jing Qin, Pheng Ann Heng	We propose a novel volumetric convolutional neural network (ConvNet) with mixed residual connections to cope with this challenging problem.
11	StructInf: Mining Structural Influence from Social Streams	Jing Zhang, Jie Tang, Yuanyi Zhong, Yuchen Mo, Juanzi Li, Guojie Song, Wendy Hall, Jimeng Sun	We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process.
12	Transitive Hashing Network for Heterogeneous Multimedia Retrieval	Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang	Existing work on cross-modal hashing assumes that heterogeneous relationship across modalities is available for learning to hash.
13	Marrying Uncertainty and Time in Knowledge Graphs	Melisachew Wudage Chekol, Giuseppe Pirró, Joerg Schoenfisch, Heiner Stuckenschmidt	The goal of this paper is to fill this gap.
14	TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning	Aleksandr Farseev, Tat-Seng Chua	Specifically, to infer personal wellness attributes, we proposed multi-source individual user profile learning framework named “TweetFit”.
15	POI2Vec: Geographical Latent Representation for Predicting Future Visitors	Shanshan Feng, Gao Cong, Bo An, Yeow Meng Chee	In this work, we propose a new latent representation model POI2Vec that is able to incorporate the geographical influence, which has been shown to be very important in modeling user mobility behavior.
16	A Dependency-Based Neural Reordering Model for Statistical Machine Translation	Christian Hadiwinoto, Hwee Tou Ng	In this paper, we present a novel reordering approach utilizing a neural network and dependency-based embeddings to predict whether the translations of two source words linked by a dependency relation should remain in the same order or should be swapped in the translated sentence.
17	Joint Identification of Network Communities and Semantics via Integrative Modeling of Network Topologies and Node Contents	Dongxiao He, Zhiyong Feng, Di Jin, Xiaobao Wang, Weixiong Zhang	We introduced a novel generative model with two closely correlated parts, one for communities and the other for semantics.
18	Random-Radius Ball Method for Estimating Closeness Centrality	Wataru Inariba, Takuya Akiba, Yuichi Yoshida	This paper focuses on a family of centrality measures including the harmonic centrality and its variants, and addresses their computational difficulty on very large graphs by presenting a new estimation algorithm named the random-radius ball (RRB) method.
19	Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes	Hideaki Kim, Tomoharu Iwata, Yasuhiro Fujiwara, Naonori Ueda	In this paper, we construct a well-timed POI recommender system that updates its recommendations in accordance with the silence, the temporal period in which no visits are made.
20	Treatment Effect Estimation with Data-Driven Variable Decomposition	Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Shiqiang Yang, Fei Wang	In this paper, we propose a Data-Driven Variable Decomposition (D$^2$VD) algorithm, which can 1) automatically separate confounders and adjustment variables with a data driven approach, and 2) simultaneously estimate treatment effect in observational studies with high dimensional variables.
21	A Declarative Approach to Data-Driven Fact Checking	Julien Leblay	In this work, we address the problem of checking the validity of claims in multiple contexts.
22	Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding	Zemin Liu, Vincent W. Zheng, Zhou Zhao, Fanwei Zhu, Kevin Chen-Chuan Chang, Minghui Wu, Jing Ying	Thus, we introduce a new concept of proximity embedding, which directly embeds the network structure between two possibly distant nodes.
23	Multi-Task Deep Learning for User Intention Understanding in Speech Interaction Systems	Yishuang Ning, Jia Jia, Zhiyong Wu, Runnan Li, Yongsheng An, Yanfeng Wang, Helen Meng	In this paper, we define Intention Prominence (IP) as the semantic combination of focus by text and emphasis by speech, and propose a multi-task deep learning framework to predict IP.
24	Understanding the Semantic Structures of Tables with a Hybrid Deep Neural Network Architecture	Kyosuke Nishida, Kugatsu Sadamitsu, Ryuichiro Higashinaka, Yoshihiro Matsuo	We propose a new deep neural network architecture, TabNet, for table type classification.
25	Radon – Rapid Discovery of Topological Relations	Mohamed Ahmed Sherif, Kevin Dreßler, Panayiotis Smeros, Axel-Cyrille Ngonga Ngomo	In this paper, we present Radon – efficient solution for the discovery of topological relations between geospatial resources according to the DE9-IM standard.
26	Web-Based Semantic Fragment Discovery for On-Line Lingual-Visual Similarity	Xiaoshuai Sun, Jiewei Cao, Chao Li, Lei Zhu, Heng Tao Shen	In this paper, we present an automatic approach for on-line discovery of visual-lingual semantic fragments from weakly labeled Internet images.
27	Exploiting both Vertical and Horizontal Dimensions of Feature Hierarchy for Effective Recommendation	Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon	To fully exploit FH, we propose a unified recommendation framework that seamlessly incorporates both vertical and horizontal dimensions for effective recommendation.
28	Phrase-Based Presentation Slides Generation for Academic Papers	Sida Wang, Xiaojun Wan, Shikang Du	In this study, we propose a phrase-based approach to generate well-structured and concise presentation slides for academic papers.
29	Community Preserving Network Embedding	Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang	In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding.
30	CLARE: A Joint Approach to Label Classification and Tag Recommendation	Yilin Wang, Suhang Wang, Jiliang Tang, Guojun Qi, Huan Liu, Baoxin Li	The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper.
31	Multiple Source Detection without Knowing the Underlying Propagation Model	Zheng Wang, Chaokun Wang, Jisheng Pei, Xiaojun Ye	In this paper, we study the multiple source detection problem when the underlying propagation model is unknown.
32	Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network	Jufeng Yang, Ming Sun, Xiaoxiao Sun	Two new algorithms are developed based on conditional probability neural network (CPNN).
33	Visual Sentiment Analysis by Attending on Local Image Regions	Quanzeng You, Hailin Jin, Jiebo Luo	In this work, we study the impact of local image regions on visual sentiment analysis.
34	Correlated Cascades: Compete or Cooperate	Ali Zarezade, Ali Khodadadi, Mehrdad Farajtabar, Hamid R. Rabiee, Hongyuan Zha	Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors.
35	Finding Critical Users for Social Network Engagement: The Collapsed k-Core Problem	Fan Zhang, Ying Zhang, Lu Qin, Wenjie Zhang, Xuemin Lin	To identify critical users for social network engagement, we propose the collapsed k-core problem: given a graph G, a positive integer k and a budget b, we aim to find b vertices in G such that the deletion of the b vertices leads to the smallest k-core.
36	Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising	Jia Zhang, Zheng Wang, Qian Li, Jialin Zhang, Yanyan Lan, Qiang Li, Xiaoming Sun	In this work, we study the guaranteed delivery model which is widely used in online advertising.
37	Expectile Matrix Factorization for Skewed Data Analysis	Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li	In this paper, we propose expectile matrix factorization by introducing asymmetric least squares, a key concept in expectile regression analysis, into the matrix factorization framework.
38	Associative Memory Using Dictionary Learning and Expander Decoding	Arya Mazumdar, Ankit Singh Rawat	Specifically, the associative memories designed in this paper can store dataset containing exp(n)n-length message vectors over a network withO(n) nodes and can tolerate Ω(n / polylog) adversarial errors.
39	An Integrated Model for Effective Saliency Prediction	Xiaoshuai Sun, Zi Huang, Hongzhi Yin, Heng Tao Shen	In this paper, we proposed an integrated model of both semantic-aware and contrast-aware saliency (SCA) combining both bottom-up and top-down cues for effective eye fixation prediction.
40	The Efficiency of the HyperPlay Technique Over Random Sampling	Michael Schofield, Michael Thielscher	We show that the HyperPlay technique, which maintains a bag of updatable models for sampling an imperfect-information game, is more efficient than taking random samples of play sequences.
41	Market Pricing for Data Streams	Melika Abolhassani, Hossein Esfandiari, MohammadTaghi Hajiaghayi, Brendan Lucier, Hadi Yami	We present an envy-free mechanism for social welfare maximization problem in the streaming setting usingO(k2l) space, wherek is the number of different goods andl is the number of available items of each good.
42	Automated Design of Robust Mechanisms	Michael Albert, Vincent Conitzer, Peter Stone	We introduce a new class of mechanisms, robust mechanisms, that is an intermediary between ex-post mechanisms and Bayesian mechanisms.
43	Incentivising Monitoring in Open Normative Systems	Natasha Alechina, Joseph Y. Halpern, Ian A. Kash, Brian Logan	We present an approach to incentivising monitoring for norm violations in open multi-agent systems such as Wikipedia.
44	Envy-Free Mechanisms with Minimum Number of Cuts	Reza Alijani, Majid Farhadi, Mohammad Ghodsi, Masoud Seddighin, Ahmad S. Tajik	We study the problem of fair division of a heterogeneous resource among strategic players.
45	Strategic Signaling and Free Information Disclosure in Auctions	Shani Alkoby, David Sarne, Igal Milchtaich	This paper analyzes the problem in the context of auctions (specifically for second-price auctions).
46	Complexity of Manipulating Sequential Allocation	Haris Aziz, Sylvain Bouveret, Jérôme Lang, Simon Mackenzie	We show that the problem is NP-complete for one manipulating agent with additive utilities and several non-manipulating agents.
47	Algorithms for Max-Min Share Fair Allocation of Indivisible Chores	Haris Aziz, Gerhard Rauchecker, Guido Schryen, Toby Walsh	In view of these non-existence and complexity results, we present a polynomial-time 2-approximation algorithm for MmS fairness for chores.
48	Nash Stability in Social Distance Games	Alkida Balliu, Michele Flammini, Giovanna Melideo, Dennis Olivetti	We consider Social Distance Games (SDGs), that is cluster formation games in which agent utilities are proportional to their harmonic centralities in the respective coalitions, i.e., to the average inverse distance from the other agents.
49	On Pareto Optimality in Social Distance Games	Alkida Balliu, Michele Flammini, Dennis Olivetti	We investigate Pareto stability in Social Distance Games, that are coalition forming games in which agents utilities are proportional to their harmonic centralities in the respective coalitions, i.e., to the average inverse distance from the other agents.
50	Team-Maxmin Equilibrium: Efficiency Bounds and Algorithms	Nicola Basilico, Andrea Celli, Giuseppe De Nittis, Nicola Gatti	In this paper, we investigate bounds of (in)efficiency of the Team-maxmin equilibrium w.r.t. the Nash equilibria and w.r.t. the Maxmin equilibrium when the team members can play correlated strategies.
51	A Study of Compact Reserve Pricing Languages	MohammadHossein Bateni, Hossein Esfandiary, Vahab Mirrokni, Saeed Seddighin	In this paper, we study the compact pricing problem.
52	Faster and Simpler Algorithm for Optimal Strategies of Blotto Game	Soheil Behnezhad, Sina Dehghani, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Saeed Seddighin	In this paper, we provide the ﬁrst polynomial-size LP formulation of the optimal strategies for the Colonel Blotto game.
53	Preference Elicitation For Participatory Budgeting	Gerdus Benade, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah	We analytically compare four preference elicitation methods — knapsack votes, rankings by value or value for money, and threshold approval votes — through the lens of implicit utilitarian voting, and find that threshold approval votes are qualitatively superior.
54	Exclusion Method for Finding Nash Equilibrium in Multiplayer Games	Kimmo Berg, Tuomas Sandholm	We present a complete algorithm for finding an epsilon-Nash equilibrium, for arbitrarily small epsilon, in games with more than two players.
55	Teams in Online Scheduling Polls: Game-Theoretic Aspects	Robert Bredereck, Jiehua Chen, Rolf Niedermeier, Svetlana Obraztsova, Nimrod Talmon	We introduce a corresponding game, where each team can declare a lower total availability in the scheduling poll in order to improve its relative attendance—the pay-off.
56	Probably Approximately Efficient Combinatorial Auctions via Machine Learning	Gianluca Brero, Benjamin Lubin, Sven Seuken	In this paper, we introduce a new design paradigm for CAs based on machine learning (ML).
57	Phragmén’s Voting Methods and Justified Representation	Markus Brill, Rupert Freeman, Svante Janson, Martin Lackner	In the late 19th century, Lars Edvard Phragmén proposed a load-balancing approach for selecting committees based on approval ballots.
58	Multiwinner Approval Rules as Apportionment Methods	Markus Brill, Jean-Francois Laslier, Piotr Skowron	We establish a link between multiwinner elections and apportionment problems by showing how approval-based multiwinner election rules can be interpreted as methods of apportionment.
59	Dynamic Thresholding and Pruning for Regret Minimization	Noam Brown, Christian Kroer, Tuomas Sandholm	In this paper, we introduce dynamic thresholding, in which a threshold is set at every iteration such that any action in the decision tree with probability below the threshold is set to zero probability.
60	Optimizing Positional Scoring Rules for Rank Aggregation	Ioannis Caragiannis, Xenophon Chatzigeorgiou, George A. Krimpas, Alexandros A. Voudouris	Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers.
61	On Markov Games Played by Bayesian and Boundedly-Rational Players	Muthukumaran Chandrasekaran, Yingke Chen, Prashant Doshi	We present a new game-theoretic framework in which Bayesian players with bounded rationality engage in a Markov game and each has private but incomplete information regarding other players’ types.
62	Bounded Rationality of Restricted Turing Machines	Lijie Chen, Pingzhong Tang, Ruosong Wang	We study our model under the context of two-person repeated games.
63	Winner Determination in Huge Elections with MapReduce	Theresa Csar, Martin Lackner, Reinhard Pichler, Emanuel Sallinger	In this work, we apply the MapReduce framework – which has been specifically designed for dealing with big data – to various versions of the winner determination problem.
64	Approximation and Parameterized Complexity of Minimax Approval Voting	Marek Cygan, Łukasz Kowalik, Arkadiusz Socała, Krzysztof Sornat	We present three results on the complexity of MINIMAX APPROVAL VOTING.
65	The Computational Complexity of Weighted Greedy Matching	Argyrios Deligkas, George B. Mertzios, Paul G. Spirakis	On the positive side, we present a randomized approximation algorithm (RGMA) for GREEDYMATCHING on a special class of weighted graphs, called bushgraphs.
66	Disarmament Games	Yuan Deng, Vincent Conitzer	In this paper, we study disarmament for two-player normal-form games.
67	The Complexity of Stable Matchings under Substitutable Preferences	Yuan Deng, Debmalya Panigrahi, Bo Waggoner	Clearly, verification is necessary for computation, but we show that it is also sufficient: specifically, given a verifier, we design a polynomial-time algorithm for computing a choice function, implying an efficient algorithm for stable matching.
68	Small Representations of Big Kidney Exchange Graphs	John P. Dickerson, Aleksandr M. Kazachkov, Ariel D. Procaccia, Tuomas Sandholm	In this paper, we observe that if the kidney exchange compatibility graph can be encoded by a constant number of patient and donor attributes, the clearing problem is solvable in polynomial time.
69	What Do Multiwinner Voting Rules Do? An Experiment Over the Two-Dimensional Euclidean Domain	Edith Elkind, Piotr Faliszewski, Jean-Francois Laslier, Piotr Skowron, Arkadii Slinko, Nimrod Talmon	We consider three applications of multiwinner voting, namely, parliamentary elections, portfolio/movie selection, and shortlisting, and use our results to understand which of our rules seem to be best suited for each application.
70	Extensive-Form Perfect Equilibrium Computation in Two-Player Games	Gabriele Farina, Nicola Gatti	We study the problem of computing an Extensive-Form Perfect Equilibrium (EFPE) in 2-player games.
71	Selfish Knapsack	Itai Feigenbaum, Matthew P. Johnson	We consider a strategic variant of the knapsack problem: the items are owned by agents, and agents can misrepresent their sets of items—either by hiding items (understating), or by reporting fake ones (overstating).
72	Obvious Strategyproofness Needs Monitoring for Good Approximations	Diodato Ferraioli, Carmine Ventre	We prove a number of bounds on the approximation guarantee of OSP mechanisms, which show that OSP can come at a significant cost.
73	Crowdsourced Outcome Determination in Prediction Markets	Rupert Freeman, Sebastien Lahaie, David M. Pennock	Motivated by the recent introduction of decentralized prediction markets, we introduce a mechanism that allows for the outcome to be determined by the votes of a group of arbiters who may themselves hold stakes in the market.
74	Security Games on a Plane	Jiarui Gan, Bo An, Yevgeniy Vorobeychik, Brian Gauch	To address this limitation, we propose a model called Security Game on a Plane (SGP) in which targets are distributed on a 2-dimensional plane, and security resources, to be allocated on the same plane, protect targets within a certain effective distance.
75	Engineering Agreement: The Naming Game with Asymmetric and Heterogeneous Agents	Jie Gao, Bo Li, Grant Schoenebeck, Fang-Yi Yu	In this paper we consider asymmetric or heterogeneous settings that complement the current literature: 1) we show that increasing asymmetry in network topology can improve convergence rates.
76	Vote Until Two of You Agree: Mechanisms with Small Distortion and Sample Complexity	Stephen Gross, Elliot Anshelevich, Lirong Xia	To design social choice mechanisms with desirable utility properties, normative properties, and low sample complexity, we propose a new randomized mechanism called 2-Agree.
77	Computing Least Cores of Supermodular Cooperative Games	Daisuke Hatano, Yuichi Yoshida	In this paper, we characterize the strong and the weak least cores of supermodular cooperative games using the theory of minimizing crossing submodular functions.
78	Heuristic Search Value Iteration for One-Sided Partially Observable Stochastic Games	Karel Horák, Branislav Bošanský, Michal Pěchouček	We present a novel algorithm for approximately solving one-sided POSGs based on the heuristic search value iteration (HSVI) for POMDPs.
79	Group Activity Selection on Social Networks	Ayumi Igarashi, Dominik Peters, Edith Elkind	We propose a new variant of the group activity selection problem (GASP), where the agents are placed on a social network and activities can only be assigned to connected subgroups.
80	Resource Graph Games: A Compact Representation for Games with Structured Strategy Spaces	Albert Xin Jiang, Hau Chan, Kevin Leyton-Brown	We propose Resource Graph Games (RGGs), the first general compact representation for games with structured strategy spaces, which is able to represent a wide range of games studied in literature.
81	Complexity of the Stable Invitations Problem	Hooyeon Lee, Vassilevska Williams	In this work, we study the complexity of SIP on a finer scale, through the lense of parameterized complexity.For the two solution concepts and the special cases where the number of friends and/or enemies is bounded above by a constant, we show that the problems belong to different complexity classes when parameterized by the size of solutions.For instance finding an individually rational invitation of sizek is W[1]-complete, yet finding a stable invitation is W[2]-complete.Moreover, when all friend and enemy relations are symmetric, finding a solution of either of the concepts becomes fixed-parameter tractable unless agents have unbounded number(s) of enemies.
82	Mechanism Design in Social Networks	Bin Li, Dong Hao, Dengji Zhao, Tao Zhou	We propose a novel auction mechanism, called information diffusion mechanism (IDM), which incentivizes the buyers to not only truthfully report their valuations on the commodity to the seller, but also further propagate the auction information to all their neighbors.
83	Optimal Personalized Defense Strategy Against Man-In-The-Middle Attack	Xiaohong Li, Shuxin Li, Jianye Hao, Zhiyong Feng, Bo An	This paper proposes a game-theoretic defense strategy from a different perspective, which aims at minimizing the loss that the whole system sustains given that the MITM attacks are inevitable.
84	Network, Popularity and Social Cohesion: A Game-Theoretic Approach	Jiamou Liu, Ziheng Wei	We follow this idea and propose a game-based model of cohesion that not only relies on the social network, but also reflects individuals’ social needs.
85	Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices	Yang Liu, Yiling Chen	In this paper, we study a sequential peer prediction problem where a data requester wants to dynamically determine the reward level to optimize the trade-off between the quality of information elicited from workers and the total expected payment.
86	An Ambiguity Aversion Model for Decision Making under Ambiguity	Wenjun Ma, Xudong Luo, Yuncheng Jiang	To the end, this paper proposes a new decision making model based on D-S theory and the emotion of ambiguity aversion.
87	Optimal Pricing for Submodular Valuations with Bounded Curvature	Takanori Maehara, Yasushi Kawase, Hanna Sumita, Katsuya Tono, Ken-ichi Kawarabayashi	In this paper, we consider the case that the valuations are submodular.
88	On Covering Codes and Upper Bounds for the Dimension of Simple Games	Martin Olsen	We present two new upper bounds both containing the Taylor-Zwicker bound as a special case.
89	Tractable Algorithms for Approximate Nash Equilibria in Generalized Graphical Games with Tree Structure	Luis E. Ortiz, Mohammad T. Irfan	We provide the first fully polynomial time approximation scheme (FPTAS) for computing an approximate mixed-strategy Nash equilibrium in graphical multi-hypermatrix games (GMhGs), which are generalizations of normal-form games, graphical games, graphical polymatrix games, and hypergraphical games.
90	Recognising Multidimensional Euclidean Preferences	Dominik Peters	We study the problem of deciding whether a given preference profile isd-Euclidean.
91	Preferences Single-Peaked on a Circle	Dominik Peters, Martin Lackner	We introduce the domain of preferences that are single-peaked on a circle, which is a generalization of the well-studied single-peaked domain.
92	Psychological Forest: Predicting Human Behavior	Ori Plonsky, Ido Erev, Tamir Hazan, Moshe Tennenholtz	We introduce a synergetic approach incorporating psychological theories and data science in service of predicting human behavior.
93	Revenue Maximization for Finitely Repeated Ad Auctions	Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu	We investigate the sample size optimization problem for Generalized Second Price auctions, which is the most widely-used mechanism in ad auctions, and make three main contributions along this line.
94	Proportional Justified Representation	Luis Sánchez-Fernández, Edith Elkind, Martin Lackner, Norberto Fernández, Jesús A. Fisteus, Pablo Basanta Val, Piotr Skowron	In this paper, we extend the work of Aziz et al. in several directions.
95	Achieving Sustainable Cooperation in Generalized Prisoner’s Dilemma with Observation Errors	Fuuki Shigenaka, Tadashi Sekiguchi, Atsushi Iwasaki, Makoto Yokoo	We deal with a generic problem that can model both the prisoner’s dilemma and the team production problem.
96	Mechanism Design for Multi-Type Housing Markets	Sujoy Sikdar, Sibel Adali, Lirong Xia	We study multi-type housing markets, where there arep ≥ 2 types of items, each agent is initially endowed one item of each type, and the goal is to design mechanisms without monetary transfer to (re)allocate items to the agents based on their preferences over bundles of items, such that each agent gets one item of each type.
97	Constrained Pure Nash Equilibria in Polymatrix Games	Sunil Simon, Dominik Wojtczak	We study the problem of checking for the existence of constrained pure Nash equilibria in a subclass of polymatrix games defined on weighted directed graphs.
98	Axiomatic Characterization of Game-Theoretic Network Centralities	Oskar Skibski, Tomasz P. Michalak, Talal Rahwan	In this paper, we focus on the game-theoretic approach to centrality analysis.
99	Social Choice Under Metric Preferences: Scoring Rules and STV	Piotr Krzysztof Skowron, Edith Elkind	In this paper, we analyze the distortion of two widely used (classes of) voting rules, namely, scoring rules and Single Transferable Vote (STV).
100	Fans Economy and All-Pay Auctions with Proportional Allocations	Pingzhong Tang, Yulong Zeng, Song Zuo	In this paper, we analyze an emerging economic form, called fans economy, in which a fan donates money to the host and gets allocated proportional to the amount of his donation (normalized by the overall amount of donation).
101	The Positronic Economist: A Computational System for Analyzing Economic Mechanisms	David Thompson, Neil Newman, Kevin Leyton-Brown	Our contribution, the Positronic Economist is a software system having two parts: (1) a Python-based language for succinctly describing mechanisms; and (2) a system that takes such descriptions as input, automatically identifies computationally useful structure, and produces a compact Action-Graph Game.
102	Non-Additive Security Games	Sinong Wang, Fang Liu, Ness Shroff	In this paper, we investigate a general security game where the utility function is defined on a collection of subsets of all targets, and provide a novel theoretical framework to show how to compactly represent such a game, efficiently compute the optimal (minimax) strategies, and characterize the complexity of this problem.
103	The Dollar Auction with Spiteful Players	Marcin Waniek, Long Tran-Thanh, Tomasz P. Michalak, Nicholas R. Jennings	In this paper, we analyse the course of an auction when participating players are spiteful, i.e., they are motivated not only by their own profit, but also by the desire to hurt the opponent.
104	Proper Proxy Scoring Rules	Jens Witkowski, Pavel Atanasov, Lyle H. Ungar, Andreas Krause	We introduce proxy scoring rules, which generalize proper scoring rules and, given access to an appropriate proxy, allow for immediate scoring of probabilistic forecasts.
105	Randomized Mechanisms for Selling Reserved Instances in Cloud	Jia Zhang, Weidong Ma, Tao Qin, Xiaoming Sun, Tie-Yan Liu	In this paper, we consider a more flexible pricing model for instance reservation, in which a customer can propose the time length and number of resources of her request, while in today’s industry, customers can only choose from several predefined reservation packages.
106	Embedded Bandits for Large-Scale Black-Box Optimization	Abdullah Al-Dujaili, S. Suresh	This paper proposes the EmbeddedHunter algorithm, which incorporates the technique in a hierarchical stochastic bandit setting, following the optimism in the face of uncertainty principle and breaking away from the multiple-run framework in which random embedding has been conventionally applied similar to stochastic black-box optimization solvers.
107	Reactive Dialectic Search Portfolios for MaxSAT	Carlos Ansótegui, Josep Pon, Meinolf Sellmann, Kevin Tierney	Metaheuristics have been developed to provide general purpose approaches for solving hard combinatorial problems.
108	Efficient Parameter Importance Analysis via Ablation with Surrogates	Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, Holger Hoos	Here, we show how the running time cost of ablation analysis, a well-known general-purpose approach for assessing parameter importance, can be reduced substantially by using regression models of algorithm performance constructed from data collected during the configuration process.
109	Problem Difficulty and the Phase Transition in Heuristic Search	Eldan Cohen, J. Christopher Beck	In this work, we perform the first empirical investigation of the phase transition phenomena for heuristic search.
110	Automatic Logic-Based Benders Decomposition with MiniZinc	Toby O. Davies, Graeme Gange, Peter J. Stuckey	We propose an automated approach that accepts an arbitrary MiniZinc model and solves it using LBBD with no additional intervention on the part of the modeller.
111	Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization	Cong Fang, Zhouchen Lin	In this paper, we study Stochastic Variance Reduced Gradient (SVRG) in the asynchronous setting.
112	A Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search	Tobias Friedrich, Timo Kötzing, Markus Wagner	In this article, we consider two classical NP-complete combinatorial optimization problems, traveling salesperson and minimum vertex cover, and study the effectiveness of different bet-and-run strategies.
113	The Simultaneous Maze Solving Problem	Stefan Funke, Andre Nusser, Sabine Storandt	We present a theoretical problem analysis, including hardness results and a cubic upper bound on the sequence length.
114	Going Beyond Primal Treewidth for (M)ILP	Robert Ganian, Sebastian Ordyniak, M. S. Ramanujan	Our main contribution is the introduction and algorithmic exploitation of two new decompositional parameters for ILP and MILP.
115	Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates	Ilija Ilievski, Taimoor Akhtar, Jiashi Feng, Christine Annette Shoemaker	In this work, we propose a new deterministic and efficient hyperparameter optimization method that employs radial basis functions as error surrogates.
116	An Exact Algorithm for the Maximum Weight Clique Problem in Large Graphs	Hua Jiang, Chu-Min Li, Felip Manya	We describe an exact branch-and-bound algorithm for the maximum weight clique problem (MWC), called WLMC, that is especially suited for large vertex-weighted graphs.
117	Learning to Prune Dominated Action Sequences in Online Black-Box Planning	Yuu Jinnai, Alex Fukunaga	We propose a method for significantly reducing the number of calls to the simulator by the search algorithm by detecting and pruning sequences of actions which are dominated by others.
118	Systematic Exploration of Larger Local Search Neighborhoods for the Minimum Vertex Cover Problem	Maximilian Katzmann, Christian Komusiewicz	We investigate the potential of exhaustively exploring larger neighborhoods in local search algorithms for Minimum Vertex Cover.
119	New Lower Bound for the Minimum Sum Coloring Problem	Clément Lecat, Corinne Lucet, Chu-Min Li	In this paper, we borrow a notion called motif, that was used in a recent work for upper bounding the minimum number of colors in an optimal solution of MSCP, to develop a new algebraic lower bound called for MSCP.
120	Anytime Anyspace AND/OR Search for Bounding the Partition Function	Qi Lou, Rina Dechter, Alexander Ihler	In this paper, we develop an anytime anyspace search algorithm taking advantage of AND/OR tree structure and optimized variational heuristics to tighten deterministic bounds on the partition function.
121	Dancing with Decision Diagrams: A Combined Approach to Exact Cover	Masaaki Nishino, Norihito Yasuda, Shin-ichi Minato, Masaaki Nagata	We propose a method to accelerate DLX.
122	Solving High-Dimensional Multi-Objective Optimization Problems with Low Effective Dimensions	Hong Qian, Yang Yu	This paper proposes a general, theoretically-grounded yet simple approach ReMO, which can scale current derivative-free MO algorithms to the high-dimensional non-convex MO functions with low effective dimensions, using random embedding.
123	Automated Data Extraction Using Predictive Program Synthesis	Mohammad Raza, Sumit Gulwani	We describe a predictive program synthesis algorithm that infers programs in a general form of extraction DSLs (domain specific languages) given input-only examples.
124	Grid Pathfinding on the 2	Nicolas Rivera, Carlos Hernández, Jorge A. Baier	This paper describes three contributions that enable the construction of effective grid path planners for extended 2k-neighborhoods.
125	Non-Monotone DR-Submodular Function Maximization	Tasuku Soma, Yuichi Yoshida	In this paper, we present a 1/(2+ε)-approximation algorithm with a running time of roughly O(n/ε log2B), wheren is the size of the ground set,B is the maximum value of a coordinate, and ε > 0 is a parameter.
126	Regret Ratio Minimization in Multi-Objective Submodular Function Maximization	Tasuku Soma, Yuichi Yoshida	In this paper, we propose two efficient methods for finding a small family of representative solutions, based on the notion of regret ratio.
127	Value Compression of Pattern Databases	Nathan R. Sturtevant, Ariel Felner, Malte Helmert	In this paper we propose a compression technique that preserves every entry, but reduces the number of bits used to store each entry, therefore limiting the values that can be represented.
128	A Fast Algorithm to Compute Maximum	Mingyu Xiao, Weibo Lin, Yuanshun Dai, Yifeng Zeng	In this paper, we study the maximumk-plex problem and propose a fast algorithm to compute maximumk-plexes by exploiting structural properties of the problem.
129	A Unified Convex Surrogate for the Schatten-	Chen Xu, Zhouchen Lin, Hongbin Zha	In this paper, we show that for anyp,p1, andp2 > 0 satisfying 1/p = 1/p1 + 1/p2, there is an equivalence between the Schatten-p norm of one matrix and the Schatten-p1 and the Schatten-p2 norms of its two factor matrices.
130	Efficient Stochastic Optimization for Low-Rank Distance Metric Learning	Jie Zhang, Lijun Zhang	Due to the PSD constraint, the computational complexity of previous algorithms per iteration is at leastO(d2) whered is the dimensionality of the data.In this paper, we develop an efficient stochastic algorithm for a class of distance metric learning problems with nuclear norm regularization, referred to as low-rank DML.
131	Examples-Rules Guided Deep Neural Network for Makeup Recommendation	Taleb Alashkar, Songyao Jiang, Shuyang Wang, Yun Fu	In this paper, we consider a fully automatic makeup recommendation system and propose a novel examples-rules guided deep neural network approach.
132	Predicting Latent Narrative Mood Using Audio and Physiologic Data	Tuka Waddah AlHanai, Mohammad Mahdi Ghassemi	In this study we utilized a combination of auditory, text, and physiological signals to predict the mood (happy or sad) of 31 narrations from subjects engaged in personal story-telling.
133	Collaborative Planning with Encoding of Users’ High-Level Strategies	Joseph Kim, Christopher J. Banks, Julie A. Shah	Specifically, we explore a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner.
134	Long-Term Trends in the Public Perception of Artificial Intelligence	Ethan Fast, Eric Horvitz	We present a set of measures that captures levels of engagement, measures of pessimism and optimism, the prevalence of specific hopes and concerns, and topics that are linked to discussions about AI over decades.
135	A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution	Arya Mazumdar, Barna Saha	In this paper, we make the first attempt to close this gap.
136	Pairwise HITS: Quality Estimation from Pairwise Comparisons in Creator-Evaluator Crowdsourcing Process	Takeru Sunahase, Yukino Baba, Hisashi Kashima	In this study, we propose a novel quality estimation method for the two-stage procedure where pairwise comparison results for pairs of artifacts are collected at the evaluation stage.
137	The Benefit in Free Information Disclosure When Selling Information to People	Shani Alkoby, David Sarne	This paper studies the benefit for information providers in free public information disclosure in settings where the prospective information buyers are people.
138	Psychologically Based Virtual-Suspect for Interrogative Interview Training	Moshe Bitan, Galit Nahari, Zvi Nisin, Ariel Roth, Sarit Kraus	In this paper, we present a Virtual-Suspect system which can be used to train inexperienced law enforcement personnel in interrogation strategies.
139	PIVE: Per-Iteration Visualization Environment for Real-Time Interactions with Dimension Reduction and Clustering	Hannah Kim, Jaegul Choo, Changhyun Lee, Hanseung Lee, Chandan K. Reddy, Haesun Park	To tackle this problem, this paper presents PIVE (Per-Iteration Visualization Environment) that supports real-time interactive visualization with machine learning.
140	JAG: A Crowdsourcing Framework for Joint Assessment and Peer Grading	Igor Labutov, Christoph Studer	Inspired by this problem in the context of education, we propose a general crowdsourcing framework that fuses open-response test-taking (content generation) and assessment into a single, streamlined process that appears to students in the form of an explicit test, but where everyone also acts as an implicit grader.
141	On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems	Besmira Nushi, Ece Kamar, Eric Horvitz, Donald Kossmann	We propose a human-in-the-loop methodology which leverages human intellect for troubleshooting system failures.
142	Capturing Dependencies among Labels and Features for Multiple Emotion Tagging of Multimedia Data	Shan Wu, Shangfei Wang, Qiang Ji	In this paper, we tackle the problem of emotion tagging of multimedia data by modeling the dependencies among multiple emotions in both the feature and label spaces.
143	On the Computation of Paracoherent Answer Sets	Giovanni Amendola, Carmine Dodaro, Wolfgang Faber, Nicola Leone, Francesco Ricca	In this paper, this lack is addressed, and several different algorithms to compute semi-stable and semi-equilibrium models are proposed and implemented into an answer set solving framework.
144	Polynomially Bounded Logic Programs with Function Symbols: A New Decidable	Vernon Asuncion, Yan Zhang, Heng Zhang	In this paper, we introduce a new decidable class of finitely ground programs called POLY-bounded programs, which, to the best of our knowledge, strictly contains all decidable classes of finitely ground programs discovered so far in the literature.
145	Abstraction in Situation Calculus Action Theories	Bita Banihashemi, Giuseppe De Giacomo, Yves Lesperance	We develop a general framework for agent abstraction based on the situation calculus and the ConGolog agent programming language.
146	Source Information Disclosure in Ontology-Based Data Integration	Michael Benedikt, Bernardo Cuenca Grau, Egor V. Kostylev	In this paper, we formalize and study the problem of determining whether a given data integration system discloses a source query to an attacker.
147	Ontology-Mediated Queries for Probabilistic Databases	Stefan Borgwardt, Ismail Ilkan Ceylan, Thomas Lukasiewicz	In this paper, we extend OpenPDBs by Datalog+/- ontologies, under which both upper and lower probabilities of queries become even more informative, enabling us to distinguish queries that were indistinguishable before.
148	Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic	Sebastian Brandt, Elem Güzel Kalaycı, Roman Kontchakov, Vladislav Ryzhikov, Guohui Xiao, Michael Zakharyaschev	We advocate datalogMTL, a datalog extension of a Horn fragment of the metric temporal logic MTL, as a language for ontology-based access to temporal log data.
149	Solving Advanced Argumentation Problems with Answer-Set Programming	Gerhard Brewka, Martin Diller, Georg Heissenberger, Thomas Linsbichler, Stefan Woltran	Solving Advanced Argumentation Problems with Answer-Set Programming
150	Checking the Consistency of Combined Qualitative Constraint Networks	Quentin Cohen-Solal, Maroua Bouzid, Alexandre Niveau	We propose a framework which encompasses loose integrations and a form of spatio-temporal reasoning.
151	Add Data into Business Process Verification: Bridging the Gap between Theory and Practice	Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Marco Montali, Sergio Tessaris	In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks.
152	Practical TBox Abduction Based on Justification Patterns	Jianfeng Du, Hai Wan, Huaguan Ma	We propose efficient methods for computing all admissible ⊆ds-minimal explanations and for computing all justification patterns, respectively.
153	The Unusual Suspects: Deep Learning Based Mining of Interesting Entity Trivia from Knowledge Graphs	Nausheen Fatma, Manoj K. Chinnakotla, Manish Shrivastava	In this paper, we propose a novel approach called DBpedia Trivia Miner (DTM) to automatically mine trivia for entities of a given domain in KGs.
154	Ontology Materialization by Abstraction Refinement in Horn SHOIF	Birte Glimm, Yevgeny Kazakov, Trung-Kien Tran	In this paper, we propose an extension of this method that is now complete for Horn SHOIF and also handles role- and equality-materialization.
155	Number Restrictions on Transitive Roles in Description Logics with Nominals	Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García, Jean Christoph Jung	We study description logics (DLs) supporting number restrictions on transitive roles.
156	Strategic Sequences of Arguments for Persuasion Using Decision Trees	Emmanuel Hadoux, Anthony Hunter	To address this, we present a general framework for representing persuasion dialogues as a decision tree, and for using decision rules for selecting moves.
157	Preferential Structures for Comparative Probabilistic Reasoning	Matthew Harrison-Trainor, Wesley H. Holliday, Thomas F. Icard, III	Thus, the same preferential structures used in the study of non-monotonic logics and belief revision may be used in the study of comparative probabilistic reasoning based on imprecise probabilities.
158	Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach	André Hernich, Julio Lemos, Frank Wolter	In this paper we propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes.
159	Diagnosability Planning for Controllable Discrete Event Systems	Hassan Ibrahim, Philippe Dague, Alban Grastien, Lina Ye, Laurent Simon	In this paper, we propose an approach to ensure the diagnosability of a partially controllable system. We have created a special benchmark and tested three proposed methods, according to the recycling level of twin plants construction, with one cost function used for plan optimality and an optional heuristics.
160	Entropic Causal Inference	Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak Hassibi	We propose an efficient greedy algorithm for the minimum entropy coupling problem, that for n=2 provably finds a local optimum.
161	SAT Encodings for Distance-Based Belief Merging Operators	Sébastien Konieczny, Jean-Marie Lagniez, Pierre Marquis	We present SAT encoding schemes for distance-based belief merging operators relying on the (possibly weighted) drastic distance or the Hamming distance between interpretations, and using sum, GMax (leximax) or GMin (leximin) as aggregation function. In order to evaluate these encoding schemes, we generated benchmarks of a time-tabling problem and translated them into belief merging instances.
162	LPMLN, Weak Constraints, and P-log	Joohyung Lee, Zhun Yang	We present a translation of LPMLN into programs with weak constraints and a translation of P-log into LPMLN, which complement the existing translations in the opposite directions.
163	Graph-Based Wrong IsA Relation Detection in a Large-Scale Lexical Taxonomy	Jiaqing Liang, Yanghua Xiao, Yi Zhang, Seung-won Hwang, Haixun Wang	In this paper, we study the problem of improving the quality for automatically constructed web-scale knowledge bases, in particular, lexical taxonomies of isA relationships.
164	On the Transitivity of Hypernym-Hyponym Relations in Data-Driven Lexical Taxonomies	Jiaqing Liang, Yi Zhang, Yanghua Xiao, Haixun Wang, Wei Wang, Pinpin Zhu	We introduce a supervised approach to detect whether transitivity holds for any given pair of hypernym-hyponym relationships.
165	Don’t Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery	Heng-Yang Lu, Lu-Yao Xie, Ning Kang, Chong-Jun Wang, Jun-Yuan Xie	Based on this idea, we propose a model named RIBS-TM which makes use of RNN for relationship learning and IDF for filtering high-frequency words.
166	The Symbolic Interior Point Method	Martin Mladenov, Vaishak Belle, Kristian Kersting	To overcome this, we introduce a rich modeling framework for optimization problems that allows convenient codification of symbolic structure.
167	Small Is Beautiful: Computing Minimal Equivalent EL Concepts	Nadeschda Nikitina, Patrick Koopmann	In this paper, we present an algorithm and a tool for computing minimal, equivalent EL concepts wrt. a given ontology.
168	Compiling Graph Substructures into Sentential Decision Diagrams	Masaaki Nishino, Norihito Yasuda, Shin-ichi Minato, Masaaki Nagata	We propose a top-down compilation algorithmfor ZSDD that represents sets of specific graph substructures, e.g.,matchings and simple paths of a graph.
169	Efficient Evaluation of Answer Set Programs with External Sources Based on External Source Inlining	Christoph Redl	In thispaper we present an alternative approach based on inliningof external atoms, motivated by existing but less general approaches for specialized formalisms such as DL-programs.
170	On Equivalence and Inconsistency of Answer Set Programs with External Sources	Christoph Redl	In this paper we provide a characteriza-tion of equivalence of HEX -programs.
171	ProjE: Embedding Projection for Knowledge Graph Completion	Baoxu Shi, Tim Weninger	In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph’s entities and edges, and through subtle, but important, changes to the standard loss function.
172	Non-Parametric Estimation of Multiple Embeddings for Link Prediction on Dynamic Knowledge Graphs	Yi Tay, Anh Tuan Luu, Siu Cheung Hui	In this paper, we propose Parallel Universe TransE (puTransE), an adaptable and robust adaptation of the translational model.
173	Causal Discovery Using Regression-Based Conditional Independence Tests	Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan	In this paper, we propose a regression-based CI test to relax the test ofx ⊥y\|Z to simpler unconditional independence tests ofx −f(Z) ⊥y−g(Z), andx−f(Z) ⊥Z ory−g(Z) ⊥Z under the assumption that the data-generating procedure follows additive noise models (ANMs).
174	An Improved Algorithm for Learning to Perform Exception-Tolerant Abduction	Mengxue Zhang, Tushar Mathew, Brendan A. Juba	Here, we present an improved algorithm for this task.
175	Trust-Sensitive Evolution of DL-Lite Knowledge Bases	Dmitriy Zheleznyakov, Evgeny Kharlamov, Ian Horrocks	We introduce a notion of s-approximation that improves the previously proposed approximations and show how to compute it for various trust-sensitive MBAs.
176	Explicit Defense Actions Against Test-Set Attacks	Scott Alfeld, Xiaojin Zhu, Paul Barford	In this paper, we consider the setting where a predictor Bob has a fixed model, and an unknown attacker Alice aims to perturb (or poison) future test instances so as to alter Bob’s prediction to her benefit.
177	Multidimensional Scaling on Multiple Input Distance Matrices	Song Bai, Xiang Bai, Longin Jan Latecki, Qi Tian	The proposed algorithm can learn the weights of views (i.e., distance matrices) automatically by exploring the consensus information and complementary nature of views.
178	ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets	Sakyajit Bhattacharya, Vaibhav Rajan, Harsh Shrivastava	We present a new algorithm for ICU mortality prediction that is designed to address the problem of imbalance, which occurs, in the context of binary classification, when one of the two classes is significantly under–represented in the data.
179	GLOMA: Embedding Global Information in Local Matrix Approximation Models for Collaborative Filtering	Chao Chen, Dongsheng Li, Qin Lv, Junchi Yan, Li Shang, Stephen M. Chu	In this paper, we propose GLOMA, a new clustering-based matrix approximation method, which can embed global information in local matrix approximation models to improve recommendation accuracy.
180	Predicting Soccer Highlights from Spatio-Temporal Match Event Streams	Tom Decroos, Vladimir Dzyuba, Jan Van Haaren, Jesse Davis	This paper presents the POGBA algorithm for automatically predicting highlights in soccer matches, which is an important task that has not yet been addressed.
181	A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems	Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, Fangxi Zhang	To address this problem, we utilize advances of learning effective representations in deep learning, and propose a hybrid model which jointly performs deep users and items’ latent factors learning from side information and collaborative filtering from the rating matrix.
182	Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation	Li Gao, Jia Wu, Chuan Zhou, Yue Hu	In this paper, we present a novel method CDUE for time-aware item recommendation, which captures the evolution of both user’s interests and item’s contents information via topic dynamics.
183	Event Video Mashup: From Hundreds of Videos to Minutes of Skeleton	Lianli Gao, Peng Wang, Jingkuan Song, Zi Huang, Jie Shao, Heng Tao Shen	We propose a submodular based content selection model that embodies both importance and diversity to depict the event from comprehensive aspects in an efficient way.
184	Soft Video Parsing by Label Distribution Learning	Xin Geng, Miaogen Ling	In this paper, we tackle the problem of segmenting out a sequence of actions from videos.
185	Active Learning with Cross-Class Similarity Transfer	Yuchen Guo, Guiguang Ding, Yue Gao, Jungong Han	In this paper, we investigate a more practical setting where the classes in source domain are related/similar to but different from the target domain classes.
186	DeepFix: Fixing Common C Language Errors by Deep Learning	Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade	In this work, we present an end-to-end solution, called DeepFix, that can fix multiple such errors in a program without relying on any external tool to locate or fix them.
187	Question Difﬁculty Prediction for READING Problems in Standard Tests	Zhenya Huang, Qi Liu, Enhong Chen, Hongke Zhao, Mingyong Gao, Si Wei, Yu Su, Guoping Hu	In this paper, we propose a novel Test-aware Attention-based Convolutional Neural Network (TACNN) framework to automatically solve this Question Difficulty Prediction (QDP) task for READING problems (a typical problem style in English tests) in standard tests.
188	Additional Multi-Touch Attribution for Online Advertising	Wendi Ji, Xiaoling Wang	In this paper, we propose an additional multi-touch attribution model (AMTA) based on two obvious assumptions: (1) the effect of an ad exposure is fading with time and (2) the effects of ad exposures on the browsing path of a user are additive.AMTA borrows the techniques from survival analysis and uses the hazard rate to measure the influence of an ad exposure.
189	Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction	Bo Jin, Haoyu Yang, Cao Xiao, Ping Zhang, Xiaopeng Wei, Fei Wang	In this paper, we formulate the DDI type prediction problem as a multitask dyadic regression problem, where the prediction of each specific DDI type is treated as a task.
190	Semi-Supervised Multi-View Correlation Feature Learning with Application to Webpage Classification	Xiao-Yuan Jing, Fei Wu, Xiwei Dong, Shiguang Shan, Songcan Chen	In this paper, we propose a novel SMFL approach, named semi-supervised multi-view correlation feature learning (SMCFL), for webpage classification.
191	Contextual RNN-GANs for Abstract Reasoning Diagram Generation	Viveka Kulharia, Arnab Ghosh, Amitabha Mukerjee, Vinay Namboodiri, Mohit Bansal	We employ the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning as well as perform initial evaluations on a next-frame prediction task of videos.
192	A Framework for Minimal Clustering Modification via Constraint Programming	Chia-Tung Kuo, S. S. Ravi, Thi-Bich-Hanh Dao, Christel Vrain, Ian Davidson	Instead, we propose to not run the algorithm again but minimally modify the existing clustering to remove the undesirable properties.
193	Knowing What to Ask: A Bayesian Active Learning Approach to the Surveying Problem	Yoad Lewenberg, Yoram Bachrach, Ulrich Paquet, Jeffrey S. Rosenschein	We propose a method for solving the problem based on a Bayesian dimensionality reduction technique.
194	ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems	DongSheng Li, Chao Chen, Qin Lv, Li Shang, Stephen M. Chu, Hongyuan Zha	Based on the uniform stability theory, we propose an expected risk minimized matrix approximation method (ERMMA), which is designed to achieve better tradeoff between optimization error and generalization error in order to reduce the expected risk of the learned MA models.
195	Learning with Feature Network and Label Network Simultaneously	Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang	To improve the generalization performance, in this paper, we propose Doubly Regularized Multi-Label learning (DRML) by exploiting feature network and label network regularization simultaneously.
196	Collaborative Company Profiling: Insights from an Employee’s Perspective	Hao Lin, Hengshu Zhu, Yuan Zuo, Chen Zhu, Junjie Wu, Hui Xiong	To this end, in this paper, we propose a method named Company Profiling based Collaborative Topic Regression (CPCTR), for learning the latent structural patterns of companies.
197	ESPACE: Accelerating Convolutional Neural Networks via Eliminating Spatial and Channel Redundancy	Shaohui Lin, Rongrong Ji, Chao Chen, Feiyue Huang	In this paper, we make the first attempt of reducing spatial and channel redundancy directly from the visual input for CNNs acceleration.
198	A Sparse Dictionary Learning Framework to Discover Discriminative Source Activations in EEG Brain Mapping	Feng Liu, Shouyi Wang, Jay Rosenberger, Jianzhong Su, Hanli Liu	In this study, we propose for the first time that the ill-posed EEG inverse problem can be formulated and solved as a sparse over-complete dictionary learning problem.
199	On Predictive Patent Valuation: Forecasting Patent Citations and Their Types	Xin Liu, Junchi Yan, Shuai Xiao, Xiangfeng Wang, Hongyuan Zha, Stephen M. Chu	This paper does not fall into the line of intensive existing work that test or apply this hypothesis, rather we aim to address the limitation of using so-far received citations for patent valuation.
200	Let Your Photos Talk: Generating Narrative Paragraph for Photo Stream via Bidirectional Attention Recurrent Neural Networks	Yu Liu, Jianlong Fu, Tao Mei, Chang Wen Chen	To deal with these challenges, we formulate the task as a sequence-to-sequence learning problem and propose a novel joint learning model by leveraging the semantic coherence in a photo stream.
201	Data-Driven Approximations to NP-Hard Problems	Anton Milan, S. Hamid Rezatofighi, Ravi Garg, Anthony Dick, Ian Reid	In this paper, we propose to learn to solve these problem from approximate examples, using recurrent neural networks (RNNs).
202	Predicting Demographics of High-Resolution Geographies with Geotagged Tweets	Omar Montasser, Daniel Kifer	In this paper, we consider the problem of predicting demographics of geographic units given geotagged Tweets that are composed within these units.
203	Finding Cut from the Same Cloth: Cross Network Link Recommendation via Joint Matrix Factorization	Arun Reddy Nelakurthi, Jingrui He	To approach the problem of cross network link recommendation, we propose to jointly decompose the user-keyword matrices from multiple social networks, while requiring them to share the same topics and user group-topic association matrices.
204	FeaBoost: Joint Feature and Label Refinement for Semantic Segmentation	Yulei Niu, Zhiwu Lu, Songfang Huang, Xin Gao, Ji-Rong Wen	We propose a novel approach, called FeaBoost, to image semantic segmentation with only image-level labels taken as weakly-supervised constraints.
205	Learning Implicit Tasks for Patient-Specific Risk Modeling in ICU	Nozomi Nori, Hisashi Kashima, Kazuto Yamashita, Susumu Kunisawa, Yuichi Imanaka	In this paper, we propose a mortality risk prediction method for ICU patients.
206	Enabling Dark Energy Science with Deep Generative Models of Galaxy Images	Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum, Jeff Schneider, Barnabas Poczos	In particular we consider variations on conditional variational autoencoder and introduce a new adversarial objective for training of conditional generative networks.
207	Unsupervised Deep Learning for Optical Flow Estimation	Zhe Ren, Junchi Yan, Bingbing Ni, Bin Liu, Xiaokang Yang, Hongyuan Zha	In this work, we explore if a deep network for flow estimation can be trained without supervision.
208	Low-Rank Linear Cold-Start Recommendation from Social Data	Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie, Darius Braziunas	Leveraging this insight, we propose Loco, a new model for cold-start recommendation based on three ingredients: (a) linear regression to learn an optimal weighting of social signals for preferences, (b) a low-rank parametrisation of the weights to overcome the high dimensionality common in social data, and (c) scalable learning of such low-rank weights using randomised SVD.
209	Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units	Wenling Shang, Justin Chiu, Kihyuk Sohn	In this work we analyze the role of Batch Normalization (BatchNorm) layers on ResNets in the hope of improving the current architecture and better incorporating other normalization techniques, such as Normalization Propagation (NormProp), into ResNets.
210	Portfolio Selection via Subset Resampling	Weiwei Shen, Jun Wang	To hasten the applicability of Markowitz’s portfolio optimization to large portfolios, in this paper, we propose a new portfolio strategy via subset resampling.
211	Beyond IID: Learning to Combine Non-IID Metrics for Vision Tasks	Yinghuan Shi, Wenbin Li, Yang Gao, Longbing Cao, Dinggang Shen	Thus, we propose to learn and integrate non-IID metrics (NIME).
212	Fast Inverse Reinforcement Learning with Interval Consistent Graph for Driving Behavior Prediction	Masamichi Shimosaka, Junichi Sato, Kazuhito Takenaka, Kentarou Hitomi	In this work, we extend them to more generic large state space models with graphs where time interval consistency of Markov decision processes are guaranteed.
213	Neural Programming by Example	Chengxun Shu, Hongyu Zhang	In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems.
214	Simultaneous Clustering and Ensemble	Zhiqiang Tao, Hongfu Liu, Yun Fu	In light of this, we propose a novel Simultaneous Clustering and Ensemble (SCE) framework to alleviate such detrimental effect, which employs the similarity matrix from raw features to enhance the co-association matrix summarized by multiple BPs.
215	A Deep Hierarchical Approach to Lifelong Learning in Minecraft	Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor	We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base.
216	Learning Attributes from the Crowdsourced Relative Labels	Tian Tian, Ning Chen, Jun Zhu	In this paper we propose an efficient method to learn human comprehensible attributes with crowdsourcing.
217	Coupling Implicit and Explicit Knowledge for Customer Volume Prediction	Jingyuan Wang, Yating Lin, Junjie Wu, Zhong Wang, Zhang Xiong	In this paper, we propose a method titled GR-NMF for jointly modeling both implicit correlations hidden inside customer volumes and explicit geographical knowledge via an integrated probabilistic framework.
218	Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation	Jinzhuo Wang, Wenmin Wang, Ronggang Wang, Wen Gao	In this paper, we propose a computer Go system that follows experts’ way of thinking and playing.
219	Multiset Feature Learning for Highly Imbalanced Data Classification	Fei Wu, Xiao-Yuan Jing, Shiguang Shan, Wangmeng Zuo, Jing-Yu Yang	We thus propose an uncorrelated cost-sensitive multiset learning (UCML) approach.
220	Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation	Cao Xiao, Ping Zhang, W. Art Chaovalitwongse, Jianying Hu, Fei Wang	We cast the ADR-drug relation structure into a three-layer hierarchical Bayesian model.
221	Modeling the Intensity Function of Point Process Via Recurrent Neural Networks	Shuai Xiao, Junchi Yan, Xiaokang Yang, Hongyuan Zha, Stephen M. Chu	We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.
222	Progressive Prediction of Student Performance in College Programs	Jie Xu, Yuli Han, Daniel Marcu, Mihaela van der Schaar	In this paper, we develop a novel algorithm that enables progressive prediction of students’ performance by adapting ensemble learning techniques and utilizing education-specific domain knowledge.
223	Bridging Video Content and Comments: Synchronized Video Description with Temporal Summarization of Crowdsourced Time-Sync Comments	Linli Xu, Chao Zhang	In this paper, we propose to generate temporal descriptions of videos by exploiting the information of crowdsourced time-sync comments which are receiving increasing popularity on many video sharing websites.
224	Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval	Erkun Yang, Cheng Deng, Wei Liu, Xianglong Liu, Dacheng Tao, Xinbo Gao	In this paper, we propose a novel deep cross-modal hashing method to generate compact hash codes through an end-to-end deep learning architecture, which can effectively capture the intrinsic relationships between various modalities.
225	Discriminative Semi-Supervised Dictionary Learning with Entropy Regularization for Pattern Classification	Meng Yang, Lin Chen	In this paper, we propose a novel discriminative semi-supervised dictionary learning model (DSSDL) by introducing discriminative representation, an identical coding of unlabeled data to the coding of testing data final classification, and an entropy regularization term.
226	Fine-Grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images	Xin Yang, Lequan Yu, Lingyun Wu, Yi Wang, Dong Ni, Jing Qin, Pheng-Ann Heng	In this paper, we attempt to address those issues with a novel framework.
227	Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay	Haiyan Yin, Sinno Jialin Pan	In this paper, we propose a new policy distillation architecture for deep reinforcement learning, where we assume that each task uses its task-specific high-level convolutional features as the inputs to the multi-task policy network.
228	Personalized Donor-Recipient Matching for Organ Transplantation	Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar	This paper proposes a novel system (ConfidentMatch) that is trained using data from electronic health records.
229	Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction	Junbo Zhang, Yu Zheng, Dekang Qi	We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the inflow and outflow of crowds in each and every region of a city.
230	Robust Manifold Matrix Factorization for Joint Clustering and Feature Extraction	Lefei Zhang, Qian Zhang, Bo Du, Dacheng Tao, Jane You	In this paper, we propose a novel clustering and feature extraction algorithm based on an unified low-rank matrix factorization framework, which suggests that the observed data matrix can be approximated by the production of projection matrix and low dimensional representation, among which the low-dimensional representation can be approximated by the cluster indicator and latent feature matrix simultaneously.
231	Discrete Personalized Ranking for Fast Collaborative Filtering from Implicit Feedback	Yan Zhang, Defu Lian, Guowu Yang	To this end, we propose a learning-based hashing framework called Discrete Personalized Ranking (DPR), to map users and items to a Hamming space, where user-item affinity can be efficiently calculated via Hamming distance.
232	Catch’Em All: Locating Multiple Diffusion Sources in Networks with Partial Observations	Kai Zhu, Zhen Chen, Lei Ying	We propose a new source localization algorithm, named Optimal-Jordan-Cover (OJC).
233	Learning Bayesian Networks with Incomplete Data by Augmentation	Tameem Adel, Cassio P. de Campos	We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach.
234	Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption	Tameem Adel, Han Zhao, Alexander Wong	Here we propose a generative domain adaptation model that allows for modelling different assumptions about this relationship, among which is a newly introduced assumption that replaces covariate shift with a possibly more realistic assumption without losing tractability due to the efficient variational inference procedure developed.
235	Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning	Apoorv Aggarwal, Sandip Ghoshal, Ankith M. S. Shetty, Suhit Sinha, Ganesh Ramakrishnan, Purushottam Kar, Prateek Jain	We present a novel method for optimizing multivariate performance measures in the MIML setting.
236	The Bernstein Mechanism: Function Release under Differential Privacy	Francesco Aldá, Benjamin I. P. Rubinstein	We address the problem of general function release under differential privacy, by developing a functional mechanism that applies under the weak assumptions of oracle access to target function evaluation and sensitivity.
237	Heavy-Tailed Analogues of the Covariance Matrix for ICA	Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher	The main contributions of this paper are (1) A practical algorithm for heavy-tailed ICA that we call HTICA.
238	Fast Generalized Distillation for Semi-Supervised Domain Adaptation	Shuang Ao, Xiang Li, Charles X. Ling	In this paper, we propose a new paradigm, called Generalized Distillation Semi-supervised Domain Adaptation (GDSDA).
239	The Option-Critic Architecture	Pierre-Luc Bacon, Jean Harb, Doina Precup	We derive policy gradient theorems for options and propose a new option-critic architecture capable of learning both the internal policies and the termination conditions of options, in tandem with the policy over options, and without the need to provide any additional rewards or subgoals.
240	Label Efficient Learning by Exploiting Multi-Class Output Codes	Maria Florina Balcan, Travis Dick, Yishay Mansour	We present a new perspective on the popular multi-class algorithmic techniques of one-vs-all and error correcting output codes.
241	Robust Partially-Compressed Least-Squares	Stephen Becker, Ban Kawas, Marek Petrik	In this paper, we investigate compressed least-squares problems and propose new models and algorithms that address the issue of error and noise introduced by compression.
242	Learning Residual Alternating Automata	Sebastian Berndt, Maciej Liśkiewicz, Matthias Lutter, Rüdiger Reischuk	In this paper we disprove this conjecture by constructing a counterexample.
243	Resource Constrained Structured Prediction	Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, Venkatesh Saligrama	We propose a novel approach based on selectively acquiring computationally costly features during test-time in order to reduce the computational cost of pre- diction with minimal performance degradation.
244	Cross-Domain Kernel Induction for Transfer Learning	Wei-Cheng Chang, Yuexin Wu, Hanxiao Liu, Yiming Yang	This paper proposes a novel framework, which does not require a shared feature space but instead uses a parallel corpus to calibrate domain-specific kernels into a unified kernel, to leverage graph-based label propagation in cross-domain settings, and to optimize semi-supervised learning based on labeled and unlabeled data in both source and target domains.
245	Informative Subspace Learning for Counterfactual Inference	Yale Chang, Jennifer G. Dy	In this paper, we address this challenge by learning subspaces that are predictive of the outcome variable for both the treatment group and control group.
246	PAC Identification of a Bandit Arm Relative to a Reward Quantile	Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan	We propose a PAC formulation for identifying an arm in an n-armed bandit whose mean is within a fixed tolerance of the m-th highest mean.
247	Classification with Minimax Distance Measures	Morteza Haghir Chehreghani	Thereby, we propose an embedding via first summing up the centered matrices and then performing an eigenvalue decomposition.
248	Latent Discriminant Analysis with Representative Feature Discovery	Gang Chen	In this paper, we propose a latent Fisher discriminant model with representative feature discovery in an semi-supervised manner.
249	Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting	Lin Chen, Hamed Hassani, Amin Karbasi	In this paper, we consider the problem of actively learning a linear classifier through query synthesis where the learner can construct artificial queries in order to estimate the true decision boundaries.
250	Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis	Zhourong Chen, Nevin L. Zhang, Dit-Yan Yeung, Peixian Chen	We present a method for learning what we callSparse Boltzmann Machines, where each hidden unit is connected to a subset of the visible units instead of all of them.
251	Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features	Zihao Chen, Luo Luo, Zhihua Zhang	In this paper, with certain restrictions on the communication allowed in the procedures, we develop tight lower bounds on communication rounds for a broad class of non-incremental algorithms under this setting.
252	OFFER: Off-Environment Reinforcement Learning	Kamil Andrzej Ciosek, Shimon Whiteson	We propose off environment reinforcement learning (OFFER), which addresses such cases by simultaneously optimising the policy and a proposal distribution over environment variables.
253	Addressing Imbalance in Multi-Label Classification Using Structured Hellinger Forests	Zachary Alan Daniels, Dimitris N. Metaxas	We introduce an extension of structured forests, a type of random forest used for structured prediction, called Sparse Oblique Structured Hellinger Forests (SOSHF).
254	Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction	Sakyasingha Dasgupta, Takayuki Osogami	We present a formulation called Gaussian DyBM, that can be seen as an extension of a vector autoregressive (VAR) model.
255	Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems	Carlo D'Eramo, Alessandro Nuara, Matteo Pirotta, Marcello Restelli	Recently, some approaches have been proposed to reduce such bias in order to get better action-value estimates, but are limited to finite problems.In this paper, we leverage on the recently proposed weighted estimator and on Gaussian process regression to derive a new method that is able to natively handle infinitely many random variables.We show how these techniques can be used to face both continuous state and continuous actions RL problems.To evaluate the effectiveness of the proposed approach we perform empirical comparisons with related approaches.
256	Scalable Multitask Policy Gradient Reinforcement Learning	Salam El Bsat, Haitham Bou Ammar, Matthew E. Taylor	This paper proposes to a novel distributed multitask RL framework, improving the scalability across many different types of tasks.
257	From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach	Sarah M. Erfani, Mahsa Baktashmotlagh, Masud Moshtaghi, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao	In this paper we considerthe problem of classifying unseen datasets, given several labeledtraining samples drawn from similar distributions.
258	A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models	Yusuf Bugra Erol, Yi Wu, Lei Li, Stuart Russell	We propose a novel nearly-black-box algorithm, the Assumed Parameter Filter (APF), a hybrid of particle filtering for state variables and assumed density filtering for parameter variables.It has the following advantages:(a) it is online and computationally efficient;(b) it is applicable to both discrete and continuous parameter spaces with arbitrary transition dynamics.On a variety of toy and real models, APF generates more accurate results within a fixed computation budget compared to several standard algorithms from the literature.
259	Structure Regularized Unsupervised Discriminant Feature Analysis	Mingyu Fan, Xiaojun Chang, Dacheng Tao	To solve this, we propose a novel algorithmic framework which performs unsupervised feature selection.
260	Self-Paced Learning: An Implicit Regularization Perspective	Yanbo Fan, Ran He, Jian Liang, Baogang Hu	In this paper, we study a group of new regularizer (named self-paced implicit regularizer) that is deduced from robust loss function.
261	Deep MIML Network	Ji Feng, Zhi-Hua Zhou	In a MIML setting, the feature representation of instances usually has big impact on the final performance; inspired by the recent deep learning studies, in this paper, we propose the DeepMIML network which exploits deep neural network formation to generate instance representation for MIML.
262	Modeling Skewed Class Distributions by Reshaping the Concept Space	Kyle D. Feuz, Diane J. Cook	We introduce an approach to learning from imbalanced class distributions that does not change the underlying data distribution.
263	On Learning High Dimensional Structured Single Index Models	Ravi Ganti, Nikhil Rao, Laura Balzano, Rebecca Willett, Robert Nowak	In this paper, we propose computationally efficient algorithms for SIM inference in high dimensions with structural constraints.
264	Local Centroids Structured Non-Negative Matrix Factorization	Hongchang Gao, Feiping Nie, Heng Huang	In this paper, we propose a novel local centroids structured NMF to address this drawback.
265	Low-Rank Factorization of Determinantal Point Processes	Mike Gartrell, Ulrich Paquet, Noam Koenigstein	In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel.
266	Robust Loss Functions under Label Noise for Deep Neural Networks	Aritra Ghosh, Himanshu Kumar, P. S. Sastry	In this paper, we provide some sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems.
267	Exploring Commonality and Individuality for Multi-Modal Curriculum Learning	Chen Gong	Therefore, this paper proposes a novel multi-modal CL algorithm by comprehensively investigating both the individuality and commonality of different modalities.
268	MPGL: An Efficient Matching Pursuit Method for Generalized LASSO	Dong Gong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, Qinfeng Shi	We propose an efficient Matching Pursuit Generalized LASSO (MPGL) method, which overcomes these issues, and is guaranteed to converge to a global optimum.
269	Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected	Aditya Gopalan, Prashanth L. A., Michael Fu, Steve Marcus	In both settings, we propose algorithms that are inspired by Upper Confidence Bound (UCB) algorithms, incorporate cost distortions, and exhibit sublinear regret assuming Holder continuous weight distortion functions.
270	Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm	Xiawei Guo, Quanming Yao, James Tin-Yau Kwok	In this paper, we propose a time and space-efficient low-rank tensor completion algorithm by using the scaled latent nuclear norm for regularization and the Frank-Wolfe (FW) algorithm for optimization.
271	Convex Co-Embedding for Matrix Completion with Predictive Side Information	Yuhong Guo	In this paper, we propose a novel matrix completion approach that exploits side information within a principled co-embedding framework.
272	Continuous Conditional Dependency Network for Structured Regression	Chao Han, Mohamed Ghalwash, Zoran Obradovic	To achieve these objectives, we proposed Continuous Conditional Dependency Network (CCDN) for structured regression.
273	Bilateral k-Means Algorithm for Fast Co-Clustering	Junwei Han, Kun Song, Feiping Nie, Xuelong Li	To address this problem, this paper proposes a novel co-clustering method named bilateral k-means algorithm (BKM) for fast co-clustering.
274	Alternating Back-Propagation for Generator Network	Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu	This paper proposes an alternating back-propagation algorithm for learning the generator network model.
275	Enumerate Lasso Solutions for Feature Selection	Satoshi Hara, Takanori Maehara	We propose an algorithm for enumerating solutions to the Lasso regression problem.In ordinary Lasso regression, one global optimum is obtained and the resulting features are interpreted as task-relevant features.However, this can overlook possibly relevant features not selected by the Lasso.With the proposed method, we can enumerate many possible feature sets for human inspection, thus recording all the important features.We prove that by enumerating solutions, we can recover a true feature set exactly under less restrictive conditions compared with the ordinary Lasso.We confirm our theoretical results also in numerical simulations.Finally, in the gene expression and the text data, we demonstrate that the proposed method can enumerate a wide variety of meaningful feature sets, which are overlooked by the global optima.
276	Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling	Daisuke Hatano, Takuro Fukunaga, Takanori Maehara, Ken-ichi Kawarabayashi	We propose a scalable and efficient algorithm for coclustering a higher-order tensor.
277	Learning Invariant Deep Representation for NIR-VIS Face Recognition	Ran He, Xiang Wu, Zhenan Sun, Tieniu Tan	This paper presents a deep convolutional network approach that uses only one network to map both NIR and VIS images to a compact Euclidean space.
278	A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression	Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low	This paper presents such an effort to advance the state of the art of sparse spectrum GP models to achieve competitive predictive performance for massive datasets.
279	Semi-Supervised Adaptive Label Distribution Learning for Facial Age Estimation	Peng Hou, Xin Geng, Zeng-Wei Huo, Jia-Qi Lv	In this paper, we propose an algorithm called Semi-supervised Adaptive Label Distribution Learning (SALDL) to solve the dilemma and improve the performance using unlabeled data for facial age estimation.
280	Sampling Beats Fixed Estimate Predictors for Cloning Stochastic Behavior in Multiagent Systems	Brian Hrolenok, Byron Boots, Tucker Hybinette Balch	We are specifically interested in building executable models (as opposed to statistical or descriptive models) because we want to reproduce and study multiagent behavior in simulation.
281	Sequential Classification-Based Optimization for Direct Policy Search	Yi-Qi Hu, Hong Qian, Yang Yu	In this paper, we adapt the classification-based optimization for sequential sampled solutions by forming the batch of reused historical solutions.
282	A Riemannian Network for SPD Matrix Learning	Zhiwu Huang, Luc Van Gool	In this paper we build a Riemannian network architecture to open up a new direction of SPD matrix non-linear learning in a deep model.
283	Asynchronous Mini-Batch Gradient Descent with Variance Reduction for Non-Convex Optimization	Zhouyuan Huo, Heng Huang	In this paper, we consider two asynchronous parallel implementations of mini-batch gradient descent method with variance reduction: one is on distributed-memory architecture and the other is on shared-memory architecture.
284	Learning Unitary Operators with Help From u(n)	Stephanie L. Hyland, Gunnar Rätsch	In this work we focus on unitary operators and describe a parametrization using the Lie algebra u(n) associated with the Lie groupU(n) ofn ×n unitary matrices.
285	Denoising Criterion for Variational Auto-Encoding Framework	Daniel Im Jiwoong Im, Sungjin Ahn, Roland Memisevic, Yoshua Bengio	In this paper, we show that injecting noise both in input and in the stochastic hidden layer can be advantageous and we propose a modified variational lower bound as an improved objective function in this setup.
286	Recovering True Classifier Performance in Positive-Unlabeled Learning	Shantanu Jain, Martha White, Predrag Radivojac	In this work, we show that the typically used performance measures such as the receiver operating characteristic curve, or the precision recall curve obtained on such data can be corrected with the knowledge of class priors; i.e., the proportions of the positive and negative examples in the unlabeled data.
287	Generalized Ambiguity Decompositions for Classification with Applications in Active Learning and Unsupervised Ensemble Pruning	Zhengshen Jiang, Hongzhi Liu, Bin Fu, Zhonghai Wu	We generalized the classic Ambiguity Decomposition from regression problems with square loss to classification problems with any loss functions that are twice differentiable, including the logistic loss in Logistic Regression, the exponential loss in Boosting methods, and the 0-1 loss in many other classification tasks.
288	Twin Learning for Similarity and Clustering: A Unified Kernel Approach	Zhao Kang, Chong Peng, Qiang Cheng	To tackle these two challenges, we propose a model to simultaneously learn cluster indicator matrix and similarity information in kernel spaces in a principled way.
289	Tunable Sensitivity to Large Errors in Neural Network Training	Gil Keren, Sivan Sabato, Björn Schuller	We propose incorporating this idea of tunable sensitivity for hard examples in neural network learning, using a new generalization of the cross-entropy gradient step, which can be used in place of the gradient in any gradient-based training method.
290	Binary Embedding with Additive Homogeneous Kernels	Saehoon Kim, Seungjin Choi	In this paper we present a completely randomized binary embedding to work with a family of additive homogeneous kernels, referred to as BE-AHK.
291	Structured Inference Networks for Nonlinear State Space Models	Rahul G. Krishnan, Uri Shalit, David Sontag	We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks.
292	Estimating Uncertainty Online Against an Adversary	Volodymyr Kuleshov, Stefano Ermon	Here, we propose techniques that assess a classification algorithm’s uncertainty via calibrated probabilities (i.e. probabilities that match empirical outcome frequencies in the long run) and which are guaranteed to be reliable (i.e. accurate and calibrated) on out-of-distribution input, including input generated by an adversary.
293	Learning Non-Linear Dynamics of Decision Boundaries for Maintaining Classification Performance	Atsutoshi Kumagai, Tomoharu Iwata	We propose a method that involves a probabilistic model for learning future classifiers for tasks in which decision boundaries nonlinearly change over time.
294	Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration	Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz	In this paper, we formulate and address the problem of informed discovery of unknown unknowns of any given predictive model where unknown unknowns occur due to systematic biases in the training data.We propose a model-agnostic methodology which uses feedback from an oracle to both identify unknown unknowns and to intelligently guide the discovery.
295	Dynamic Action Repetition for Deep Reinforcement Learning	Aravind S. Lakshminarayanan, Sahil Sharma, Balaraman Ravindran	In this paper, we propose a new framework – Dynamic Action Repetition which changes Action Repetition Rate (the time scale of repeating an action) from a hyper-parameter of an algorithm to a dynamically learnable quantity.
296	Playing FPS Games with Deep Reinforcement Learning	Guillaume Lample, Devendra Singh Chaplot	We present a method to augment these models to exploit game feature information such as the presence of enemies or items, during the training phase.
297	Transfer Reinforcement Learning with Shared Dynamics	Romain Laroche, Merwan Barlier	This article addresses a particular Transfer Reinforcement Learning (RL) problem: when dynamics do not change from one task to another, and only the reward function does.
298	Transfer Learning for Deep Learning on Graph-Structured Data	Jaekoo Lee, Hyunjae Kim, Jongsun Lee, Sungroh Yoon	In this paper, we attempt to advance deep learning for graph-structured data by incorporating another component: transfer learning.
299	Efficient Online Model Adaptation by Incremental Simplex Tableau	Zhixian Lei, Xuehan Ye, Yongcai Wang, Deying Li, Jia Xu	This paper proposes an novel online model adaptation framework for not only efficiency but also optimal online adaptation.
300	Multivariate Hawkes Processes for Large-Scale Inference	Rémi Lemonnier, Kevin Scaman, Argyris Kalogeratos	In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems, both in the number of events in the observed historyn and the number of event typesd (i.e. dimensions).
301	Self-Paced Multi-Task Learning	Changsheng Li, Junchi Yan, Fan Wei, Weishan Dong, Qingshan Liu, Hongyuan Zha	Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we propose a novel multi-task learning framework that attempts to learn the tasks by simultaneously taking into consideration the complexities of both tasks and instances per task.
302	Infinitely Many-Armed Bandits with Budget Constraints	Haifang Li, Yingce Xia	We propose an algorithm named RCB-I to this new problem, in which the player first randomly picksK arms, whose order is sub-linear in terms ofB, and then runs the algorithm for the finite-arm setting on the selected arms.
303	Sparse Subspace Clustering by Learning Approximation ℓ0 Codes	Jun Li, Yu Kong, Yun Fu	Two sufficient conditions are presented to guarantee that our method can give a subspace-preserving affinity.
304	Riemannian Submanifold Tracking on Low-Rank Algebraic Variety	Qian Li, Zhichao Wang	This paper proposes a novel algorithm RIST that exploits the algebraic variety of low-rank manifold for matrix recovery.
305	Large Graph Hashing with Spectral Rotation	Xuelong Li, Di Hu, Feiping Nie	In this paper, we propose to impose a so-called spectral rotation technique to the spectral hashing objective, which could transform the candidate solution into a new one that better approximates the discrete one.
306	Low-Rank Tensor Completion with Total Variation for Visual Data Inpainting	Xutao Li, Yunming Ye, Xiaofei Xu	In this paper, we argue that low-rank constraint, albeit useful, is not effective enough to exploit the local smooth and piecewise priors of visual data.
307	Learning Safe Prediction for Semi-Supervised Regression	Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou	In this work we consider the learning of a safe prediction from multiple semi-supervised regressors, which is not worse than a direct supervised learner with only labeled data.
308	A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis	Zhe Li, Tianbao Yang, Lijun Zhang, Rong Jin	This paper aims to provide a sharp excess risk guarantee for learning a sparse linear model without any assumptions about the strong convexity of the expected loss and the sparsity of the optimal solution in hindsight.
309	Balanced Clustering with Least Square Regression	Hanyang Liu, Junwei Han, Feiping Nie, Xuelong Li	In this paper, we propose a novel and simple method for clustering, referred to as the Balanced Clustering with Least Square regression (BCLS), to minimize the least square linear regression, with a balance constraint to regularize the clustering model.
310	Ordinal Constrained Binary Code Learning for Nearest Neighbor Search	Hong Liu, Rongrong Ji, Yongjian Wu, Feiyue Huang	To handle these problems, we propose a novel ranking-preserving hashing method, dubbed Ordinal Constraint Hashing (OCH), which efficiently learns the optimal hashing functions with a graph-based approximation to embed the ordinal relations.
311	Sparse Deep Transfer Learning for Convolutional Neural Network	Jiaming Liu, Yali Wang, Yu Qiao	There are three main contributions in this work.
312	Cost-Sensitive Feature Selection via F-Measure Optimization Reduction	Meng Liu, Chang Xu, Yong Luo, Chao Xu, Yonggang Wen, Dacheng Tao	In this paper, we present a novel cost-sensitive feature selection (CSFS) method which optimizes F-measure instead of accuracy to take class imbalance issue into account.
313	Multiple Kernel k-Means with Incomplete Kernels	Xinwang Liu, Miaomiao Li, Lei Wang, Yong Dou, Jianping Yin, En Zhu	This paper proposes a simple while effective algorithm to address this issue.
314	Optimal Neighborhood Kernel Clustering with Multiple Kernels	Xinwang Liu, Sihang Zhou, Yueqing Wang, Miaomiao Li, Yong Dou, En Zhu, Jianping Yin	To address these issues, we propose an optimal neighborhood kernel clustering (ONKC) algorithm to enhance the representability of the optimal kernel and strengthen the negotiation between kernel learning and clustering.
315	Generalization Analysis for Ranking Using Integral Operator	Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang	In this paper, we derive novel generalization bounds for the regularized ranking in reproducing kernel Hilbert space via integral operator of kernel function.
316	Infinite Kernel Learning: Generalization Bounds and Algorithms	Yong Liu, Shizhong Liao, Hailun Lin, Yinliang Yue, Weiping Wang	In this paper, we propose a new kernel learning method based on a novel measure of generalization error, called principal eigenvalue proportion (PEP), which can learn the optimal kernel with sharp generalization bounds over the convex hull of a possibly infinite set of basic kernels.
317	Accelerated Variance Reduced Stochastic ADMM	Yuanyuan Liu, Fanhua Shang, James Cheng	To bridge this gap, we introduce the momentum acceleration trick for batch optimization into the stochastic variance reduced gradient based ADMM (SVRG-ADMM), which leads to an accelerated (ASVRG-ADMM) method.
318	Semi-Supervised Classifications via Elastic and Robust Embedding	Yun Liu, Yiming Guo, Hua Wang, Feiping Nie, Heng Huang	In the paper, we propose an efficient optimization algorithm to solve a more general problem, based on which we find the optimal solution to the derived problem.
319	Approximate Conditional Gradient Descent on Multi-Class Classification	Zhuanghua Liu, Ivor Tsang	In this work, we study the problem of approximating the Frank-Wolfe algorithm on the large-scale multi-class classification problem which is a typical application of the Frank-Wolfe algorithm.
320	Probabilistic Non-Negative Matrix Factorization and Its Robust Extensions for Topic Modeling	Minnan Luo, Feiping Nie, Xiaojun Chang, Yi Yang, Alexander Hauptmann, Qinghua Zheng	In this paper, we follow the generative procedure of topic model and learn the topic-word distribution and topics distribution via directly approximating the word-document co-occurrence matrix with matrix decomposition technique.
321	Active Search for Sparse Signals with Region Sensing	Yifei Ma, Roman Garnett, Jeff Schneider	We propose an algorithm that actively collects data to search for sparse signals using only noisy measurements of the average values on rectangular regions (including single points), based on the greedy maximization of information gain.
322	Where to Add Actions in Human-in-the-Loop Reinforcement Learning	Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popović	Therefore, we propose a framework in which a human adds actions to a reinforcement learning system over time to boost performance.
323	Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction	Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu	In this paper, we propose asynchronous ProxSVRG (Async-ProxSVRG) and asynchronous ProxSVRCD (Async-ProxSVRCD) algorithms, and prove that Async-ProxSVRG can achieve near linear speedup when the training data is sparse, while Async-ProxSVRCD can achieve near linear speedup regardless of the sparse condition, as long as the number of block partitions are appropriately set.
324	Generalization Error Bounds for Optimization Algorithms via Stability	Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu	In this paper, we investigate on this issue, by using stability as a tool.
325	When and Why Are Deep Networks Better Than Shallow Ones?	Hrushikesh Mhaskar, Qianli Liao, Tomaso Poggio	When and Why Are Deep Networks Better Than Shallow Ones?
326	Lifted Inference for Convex Quadratic Programs	Martin Mladenov, Leonard Kleinhans, Kristian Kersting	This raises the question, whether this holds for optimization problems in general.Here we show that for a large classof optimization methods this is actually the case.Specifically, we introduce the concept of fractionalsymmetries of convex quadratic programs (QPs),which lie at the heart of many AI and machine learning approaches,and exploit it to lift, i.e., to compress QPs.These lifted QPs can then be tackled with the usual optimization toolbox (off-the-shelf solvers, cutting plane algorithms,stochastic gradients etc.).
327	Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions	Alejandro Molina, Sriraam Natarajan, Kristian Kersting	We present algorithms for learning tree PSPNs from data as well as for tractable inference via symbolic evaluation.
328	Deep Collective Inference	John Moore, Jennifer Neville	In this paper, we provide an end-to-end learning framework using RNNs for collective inference.
329	Streaming Classification with Emerging New Class by Class Matrix Sketching	Xin Mu, Feida Zhu, Juan Du, Ee-Peng Lim, Zhi-Hua Zhou	In this paper, the proposed method dynamically maintains two low-dimensional matrix sketches to 1) detect emerging new classes; 2) classify known classes; and 3) update the model in the data stream.
330	Deep Hashing: A Joint Approach for Image Signature Learning	Yadong Mu, Zhu Liu	Our technical contributions in this paper are two-folds: 1) deep network optimization is often achieved by gradient propagation, which critically requires a smooth objective function.
331	Tsallis Regularized Optimal Transport and Ecological Inference	Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen	We unify the two main approaches to optimal transport, namely Monge-Kantorovitch and Sinkhorn-Cuturi, into what we define as Tsallis regularized optimal transport (TROT).
332	The Multivariate Generalised von Mises Distribution: Inference and Applications	Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner	These models can leverage standard modelling tools (e.g. kernel functions and automatic relevance determination).
333	Querying Partially Labelled Data to Improve a K-nn Classifier	Vu-Linh Nguyen, Sébastien Destercke, Marie-Helene Masson	In this paper, we propose querying strategies of partial labels for the well-known K-nn classifier.
334	Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours	Feiping Nie, Guohao Cai, Xuelong Li	In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously.
335	Multiclass Capped ℓp-Norm SVM for Robust Classifications	Feiping Nie, Xiaoqian Wang, Heng Huang	To address this problem, we proposed a novel capped Lp-norm SVM classification model by utilizing the capped `p-norm based hinge loss in the objective which can deal with both light and heavy outliers.
336	Unsupervised Large Graph Embedding	Feiping Nie, Wei Zhu, Xuelong Li	LPP and SR are two different linear spectral based methods, however, we discover that LPP and SR are equivalent, if the symmetric similarity matrix is doubly stochastic, Positive Semi-Definite (PSD) and with rank p, where p is the reduced dimension.
337	Matching Node Embeddings for Graph Similarity	Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis	In this paper, we compare graphs based on their global properties as these are captured by the eigenvectors of their adjacency matrices.
338	Inductive Pairwise Ranking: Going Beyond the	U.N. Niranjan, Arun Rajkumar	We propose and characterize a very broad class of preference matrices giving rise to the Feature Low Rank (FLR) model, which subsumes several models ranging from the classic Bradley–Terry–Luce (BTL) (Bradley and Terry 1952) and Thurstone (Thurstone 1927) models to the recently proposed blade-chest (Chen and Joachims 2016) and generic low-rank preference (Rajkumar and Agarwal 2016) models.
339	Active Search in Intensionally Specified Structured Spaces	Dino Oglic, Roman Garnett, Thomas Gaertner	We consider an active search problem in intensionally specified structured spaces.
340	Top-k Hierarchical Classification	Sechan Oh	In this paper, we define a top-k hierarchical loss function using a real world application.
341	Unimodal Thompson Sampling for Graph-Structured Arms	Stefano Paladino, Francesco Trovó, Marcello Restelli, Nicola Gatti	We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure.
342	Accelerated Gradient Temporal Difference Learning	Yangchen Pan, Adam White, Martha White	In this paper, we propose a new family of accelerated gradient TD (ATD) methods that (1) provide similar data efficiency benefits to least-squares methods, at a fraction of the computation and storage (2) significantly reduce parameter sensitivity compared to linear TD methods, and (3) are asymptotically unbiased.
343	A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization	Hanyang Peng, Yong Fan	Capitalizing on the existing sparsity regularized feature selection methods, we propose a general sparsity feature selection (GSR-FS) algorithm that optimizes a ℓ2,r (0 < r ≤ 2) based loss function with a ℓ2,p-norm (0 <p ≤ 2) sparse regularization.
344	Cascade Subspace Clustering	Xi Peng, Jiashi Feng, Jiwen Lu, Wei-Yun Yau, Zhang Yi	In this paper, we recast the subspace clustering as a verification problem.
345	Column Networks for Collective Classification	Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh	We present Column Network (CLN), a novel deep learning model for collective classification in multi-relational domains.
346	A General Clustering Agreement Index: For Comparing Disjoint and Overlapping Clusters	Reihaneh Rabbany, Osmar R. Zaïane	In this paper, we present a general Clustering Agreement Index (CAI) for comparing disjoint and overlapping clusterings.
347	Non-Negative Inductive Matrix Completion for Discrete Dyadic Data	Piyush Rai	We present a non-negative inductive latent factor model for binary- and count-valued matrices containing dyadic data, with side information along the rows and/or the columns of the matrix.
348	Online Active Linear Regression via Thresholding	Carlos Riquelme, Ramesh Johari, Baosen Zhang	Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds.
349	Adaptive Proximal Average Approximation for Composite Convex Minimization	Li Shen, Wei Liu, Junzhou Huang, Yu-Gang Jiang, Shiqian Ma	We propose a fast first-order method to solve multi-term nonsmooth composite convex minimization problems by employing a recent proximal average approximation technique and a novel adaptive parameter tuning technique.
350	Random Features for Shift-Invariant Kernels with Moment Matching	Weiwei Shen, Zhihui Yang, Jun Wang	In this paper, we present a novel sampling algorithm powered by moment matching techniques to reduce the variance of random features.
351	Compressed K-Means for Large-Scale Clustering	Xiaobo Shen, Weiwei Liu, Ivor Tsang, Fumin Shen, Quan-Sen Sun	In this paper, we propose a novel clustering method, dubbed compressed k-means (CKM), for fast large-scale clustering.
352	Patch Reordering: A NovelWay to Achieve Rotation and Translation Invariance in Convolutional Neural Networks	Xu Shen, Xinmei Tian, Shaoyan Sun, Dacheng Tao	To enable the model to focus on learning the content of objects other than their locations, we propose to conduct patch ranking of the feature maps before feeding them into the next layer.
353	Asymmetric Discrete Graph Hashing	Xiaoshuang Shi, Fuyong Xing, Kaidi Xu, Manish Sapkota, Lin Yang	To address these two problems, in this paper, we propose a novel yetsimple supervised graph based hashing method, asymmetric discrete graph hashing, by preserving the asymmetric discrete constraint and building an asymmetric affinity matrix to learn compact binary codes.Specifically, we utilize two different instead of identical discrete matrices to better preserve the similarity of the graph with short binary codes.
354	Spectral Clustering with Brainstorming Process for Multi-View Data	Jeong-Woo Son, Junkey Jeon, Alex Lee, Sun-Joong Kim	This paper proposes a new spectral clustering method to deal with multi-view data and dependencies among views.
355	Parameter Free Large Margin Nearest Neighbor for Distance Metric Learning	Kun Song, Feiping Nie, Junwei Han, Xuelong Li	We introduce a novel supervised metric learning algorithm named parameter free large margin nearest neighbor (PFLMNN) which can be seen as an improvement of the classical large margin nearest neighbor (LMNN) algorithm.
356	Multilinear Regression for Embedded Feature Selection with Application to fMRI Analysis	Xiaonan Song, Haiping Lu	In this paper, we are interested in embedded feature selection for multidimensional data, wherein (1) there is no need to reshape the multidimensional data into vectors and (2) structural information from multiple dimensions are taken into account.
357	Distributed Negative Sampling for Word Embeddings	Stergios Stergiou, Zygimantas Straznickas, Rolina Wu, Kostas Tsioutsiouliklis	In this paper we investigate one of its core components, Negative Sampling, and propose efficient distributed algorithms that allow us to scale to vocabulary sizes of more than 1 billion unique words and corpus sizes of more than 1 trillion words.
358	Label-Free Supervision of Neural Networks with Physics and Domain Knowledge	Russell Stewart, Stefano Ermon	We introduce a new approach to supervising neural networks by specifying constraints that should hold over the output space, rather than direct examples of input-output pairs.
359	Unsupervised Learning with Truncated Gaussian Graphical Models	Qinliang Su, Xuejun Liao, Chunyuan Li, Zhe Gan, Lawrence Carin	In this paper, we introduce a novel variant of GGMs, which relaxes the Gaussian restriction and yet admits efficient inference.
360	Automatic Curriculum Graph Generation for Reinforcement Learning Agents	Maxwell Svetlik, Matteo Leonetti, Jivko Sinapov, Rishi Shah, Nick Walker, Peter Stone	To address this limitation, we introduce a method to generate a curriculum based on task descriptors and a novel metric of transfer potential.
361	Self-Correcting Models for Model-Based Reinforcement Learning	Erik Talvitie	This paper theoretically analyzes this approach, illuminates settings in which it is likely to be effective or ineffective, and presents a novel error bound, showing that a model’s ability to self-correct is more tightly related to MBRL performance than one-step prediction error.
362	Distant Domain Transfer Learning	Ben Tan, Yu Zhang, Sinno Jialin Pan, Qiang Yang	In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL).
363	Confidence-Rated Discriminative Partial Label Learning	Cai-Zhi Tang, Min-Ling Zhang	In this paper, a boosting-style partial label learning approach is proposed to enabling confidence-rated discriminative modeling.
364	Cross-Domain Ranking via Latent Space Learning	Jie Tang, Wendy Hall	In this paper, we present a unified model: BayCDR for cross-domain ranking.
365	How to Train a Compact Binary Neural Network with High Accuracy?	Wei Tang, Gang Hua, Liang Wang	We propose to use a scale layer to bring it to normal.
366	Policy Search with High-Dimensional Context Variables	Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama	In this paper, we propose a contextual policy search method in the model-based relative entropy stochastic search framework with integrated dimensionality reduction.
367	Coactive Critiquing: Elicitation of Preferences and Features	Stefano Teso, Paolo Dragone, Andrea Passerini	In this paper we propose an approach to preference elicitation suited for this scenario.
368	Importance Sampling with Unequal Support	Philip S. Thomas, Emma Brunskill	In this paper we propose a new variant of importance sampling that can reduce the variance of importance samplingbased estimates by orders of magnitude when the supports of the training and testing distributions differ.
369	Achieving Privacy in the Adversarial Multi-Armed Bandit	Aristide Charles Yedia Tossou, Christos Dimitrakakis	In this paper, we improve the previously best known regret bound to achieve ε-differential privacy in oblivious adversarial bandits from O(T2/3 /ε) to O(√T lnT/ε).
370	Thompson Sampling for Stochastic Bandits with Graph Feedback	Aristide C. Y. Tossou, Christos Dimitrakakis, Devdatt Dubhashi	As we show in this paper, it has excellent performance in problems with graph feedback, even when the graph structure itself is unknown and/or changing. We present a simple set of algorithms based on Thompson Sampling for stochastic bandit problems with graph feedback.
371	Selecting Sequences of Items via Submodular Maximization	Sebastian Tschiatschek, Adish Singla, Andreas Krause	In this paper we introduce a novel class of utility functions over sequences of items, strictly generalizing the commonly used class of submodular set functions.
372	Variable Kernel Density Estimation in High-Dimensional Feature Spaces	Christiaan Maarten van der Walt, Etienne Barnard	In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces.
373	Regularization for Unsupervised Deep Neural Nets	Baiyang Wang, Diego Klabjan	We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting.
374	Relational Deep Learning: A Deep Latent Variable Model for Link Prediction	Hao Wang, Xingjian Shi, Dit-Yan Yeung	We propose to utilize the product of Gaussians (PoG) structure in RDL to relate the inferences on different variables and derive a generalized variational inference algorithm for learning the variables and predicting the links.
375	Factorization Bandits for Interactive Recommendation	Huazheng Wang, Qingyun Wu, Hongning Wang	We perform online interactive recommendation via a factorization-based bandit algorithm.
376	Latent Smooth Skeleton Embedding	Li Wang, Qi Mao, Ivor W. Tsang	To overcome this issue, we propose a novel probabilistic structured learning model to learn the density of latent embedding given high-dimensional data and its neighborhood graph.
377	Polynomial Optimization Methods for Matrix Factorization	Po-Wei Wang, Chun-Liang Li, J. Zico Kolter	In this paper we present an approach based upon polynomial optimization techniques that both improves the convergence time of matrix factorization algorithms and helps them escape from local optima.
378	Two-Dimensional PCA with F-Norm Minimization	Qianqian Wang, Quanxue Gao	To solve F-2DPCA, we propose a fast iterative algorithm, which has a closed-form solution in each iteration, and prove its convergence.
379	Feature Selection Guided Auto-Encoder	Shuyang Wang, Zhengming Ding, Yun Fu	In this paper, we propose a novel algorithm, Feature Selection Guided Auto-Encoder, which is a unified generative model that integrates feature selection and auto-encoder together.
380	Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation	Wei Wang, Hao Wang, Chen Zhang, Yang Gao	In this paper, we focus on semi-supervised domain adaptation and explicitly extend the applied range of unlabeled target samples into the combination of distribution alignment and adaptive classifier learning.
381	Fast Online Incremental Learning on Mixture Streaming Data	Yi Wang, Xin Fan, Zhongxuan Luo, Tianzhu Wang, Maomao Min, Jiebo Luo	The explosion of streaming data poses challenges to feature learning methods including linear discriminant analysis (LDA).
382	Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation	Yingfei Wang, Hua Ouyang, Chu Wang, Jianhui Chen, Tsvetan Asamov, Yi Chang	By the adaptation of a minimum-cost maximum-flow network, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability.With its potential to work with whole-page recommendation and any probabilistic models, to illustrate the effectiveness of our method, we focus on Gaussian process optimization and a contextual setting where click-through rate is predicted using logistic regression.
383	Unbiased Multivariate Correlation Analysis	Yisen Wang, Simone Romano, Vinh Nguyen, James Bailey, Xingjun Ma, Shu-Tao Xia	In this paper, we propose an unbiased multivariate correlation measure, called UMC, which satisfies all the above criteria.
384	Beyond RPCA: Flattening Complex Noise in the Frequency Domain	Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao	This paper presents a more flexible approach to modeling complex noise by investigating their properties in the frequency domain.
385	Improving Efficiency of SVM	Zeyi Wen, Bin Li, Ramamohanarao Kotagiri, Jian Chen, Yawen Chen, Rui Zhang	In this paper, we propose three algorithms that reuse the h-th SVM for improving the efficiency of training the (h+1)-th SVM.
386	Rank Ordering Constraints Elimination with Application for Kernel Learning	Ying Xie, Chris H. Q. Ding, Yihong Gong, Zongze Wu	On seven datasets,our approach reduces the computational time by orders of magnitudes as compared to the current standardquadratically constrained quadratic programming(QCQP) optimization approach.
387	Solving Indefinite Kernel Support Vector Machine with Difference of Convex Functions Programming	Hai-Ming Xu, Hui Xue, Xiao-Hong Chen, Yun-Yun Wang	In this paper, we directly focus on the non-convex primal form of IKSVM and propose a novel algorithm termed as IKSVM-DC.
388	Cleaning the Null Space: A Privacy Mechanism for Predictors	Ke Xu, Tongyi Cao, Swair Shah, Crystal Maung, Haim Schweitzer	We describe two algorithms aimed at providing such privacy when the predictors have a linear operator in the first stage.
389	Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee	Yi Xu, Haiqin Yang, Lijun Zhang, Tianbao Yang	In this paper, we address learning problems for high dimensional data.
390	A General Efficient Hyperparameter-Free Algorithm for Convolutional Sparse Learning	Zheng Xu, Junzhou Huang	Among all structured sparse models, we found an interesting fact that most structured sparse properties could be captured by convolution operators, most famous ones being total variation and wavelet sparsity.
391	Multi-View Correlated Feature Learning by Uncovering Shared Component	Xiaowei Xue, Feiping Nie, Sen Wang, Xiaojun Chang, Bela Stantic, Min Yao	In this paper, we propose a new multi-view feature learning algorithm that simultaneously analyzes features from different views.
392	A Framework of Online Learning with Imbalanced Streaming Data	Yan Yan, Tianbao Yang, Yi Yang, Jianhui Chen	This work proposes a general framework for online learning with imbalanced streaming data, where examples are coming sequentially and models are updated accordingly on-the-fly.
393	TaGiTeD: Predictive Task Guided Tensor Decomposition for Representation Learning from Electronic Health Records	Kai Yang, Xiang Li, Haifeng Liu, Jing Mei, Guotong Xie, Junfeng Zhao, Bing Xie, Fei Wang	In this paper, we propose an algorithm called Predictive Task Guided Tensor Decomposition (TaGiTeD), to analyze EHRs.
394	Deep Learning for Fixed Model Reuse	Yang Yang, De-Chuan Zhan, Ying Fan, Yuan Jiang, Zhi-Hua Zhou	In this paper, we propose a more thorough model reuse scheme, FMR (Fixed Model Reuse).
395	Learning Deep Latent Space for Multi-Label Classification	Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, Yu-Chiang Frank Wang	We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task.
396	A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information	Hsiang-Fu Yu, Hsin-Yuan Huang, Inderjit Dhillon, Chih-Jen Lin	In this work we consider a generalization of one-class MF so that two types of side information are incorporated and a general convex loss function can be used.
397	SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient	Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu	In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems.
398	CBRAP: Contextual Bandits with RAndom Projection	Xiaotian Yu, Michael R. Lyu, Irwin King	In this paper, in order to attack the above two challenges effectively, we develop an algorithm of Contextual Bandits via RAndom Projection (CBRAP) in the setting of linear payoffs, which works especially for high-dimensional contextual data.
399	An Exact Penalty Method for Binary Optimization Based on MPEC Formulation	Ganzhao Yuan, Bernard Ghanem	To solve this problem, we propose a new class of continuous optimization techniques, which is based on Mathematical Programming with Equilibrium Constraints (MPECs).
400	Scalable Feature Selection via Distributed Diversity Maximization	Sepehr Abbasi Zadeh, Mehrdad Ghadiri, Vahab Mirrokni, Morteza Zadimoghaddam	Thus, in this paper, we introduce a novel vertically distributable feature selection method in order to speed up this process and be able to handle very large datasets in a scalable manner.
401	Fast Compressive Phase Retrieval under Bounded Noise	Hongyang Zhang, Shan You, Zhouchen Lin, Chao Xu	In this work, with a particular set of sensing vectors ai’s, we give a provable algorithm that is robust to any bounded yet unstructured deterministic noise.
402	Query-Efficient Imitation Learning for End-to-End Simulated Driving	Jiakai Zhang, Kyunghyun Cho	In this paper, we propose an extension of the DAgger, called SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous driving.
403	Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning	Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu	This paper proposes a learning strategy that embeds object-part concepts into a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually transform the pre-trained CNN into a semantically interpretable graphical model for hierarchical object understanding.
404	Universum Prescription: Regularization Using Unlabeled Data	Xiang Zhang, Yann LeCun	This paper shows that simply prescribing “none of the above” labels to unlabeled data has a beneficial regularization effect to supervised learning.
405	Learning Sparse Task Relations in Multi-Task Learning	Yu Zhang, Qiang Yang	In this paper, we focus on learning sparse task relations.
406	Multi-View Clustering via Deep Matrix Factorization	Handong Zhao, Zhengming Ding, Yun Fu	In this paper, we present a deep matrix factorization framework for MVC, where semi-nonnegative matrix factorization is adopted to learn the hierarchical semantics of multi-view data in a layer-wise fashion.
407	SCOPE: Scalable Composite Optimization for Learning on Spark	Shen-Yi Zhao, Ru Xiang, Ying-Hao Shi, Peng Gao, Wu-Jun Li	In this paper, we propose a novel DSO method, called scalable composite optimization for learning (SCOPE), and implement it on the fault-tolerant distributed platform Spark.
408	Lock-Free Optimization for Non-Convex Problems	Shen-Yi Zhao, Gong-Duo Zhang, Wu-Jun Li	In this paper, we provide the theoretical proof about the convergence of two representative LF-PSGD methods, Hogwild!
409	Scalable Graph Embedding for Asymmetric Proximity	Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao	In this paper, we propose an asymmetric proximity preserving (APP) graph embedding method via random walk with restart, which captures both asymmetric and high-order similarities between node pairs.
410	Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations	Yang Zhou, Haiping Lu, Yiu-ming Cheung	To overcome these drawbacks, we propose BPCCA, a new bilinear extension of PCCA, by introducing a hybrid joint model.
411	Parametric Dual Maximization for Non-Convex Learning Problems	Yuxun Zhou, Zhaoyi Kang, Costas J. Spanos	In this paper, we propose a novel learning procedure, namely Parametric Dual Maximization(PDM), that can approach global optimality efficiently with user specified approximation levels.
412	One-Step Spectral Clustering via Dynamically Learning Affinity Matrix and Subspace	Xiaofeng Zhu, Wei He, Yonggang Li, Yang Yang, Shichao Zhang, Rongyao Hu, Yonghua Zhu	This paper proposes a one-step spectral clustering method by learning an intrinsic affinity matrix (i.e., the clustering result) from the low-dimensional space (i.e., intrinsic subspace) of original data.
413	Multi-Kernel Low-Rank Dictionary Pair Learning for Multiple Features Based Image Classification	Xiaoke Zhu, Xiao-Yuan Jing, Fei Wu, Di Wu, Li Cheng, Sen Li, Ruimin Hu	In this paper, we propose a novel multi-kernel DL approach, named multi-kernel low-rank dictionary pair learning (MKLDPL).
414	Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning	Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou	In this paper, we propose the first approach to discover multiple novel labels in MIML problem using an efficient augmented lagrangian optimization, which has a bag-dependent loss term and a bag-independent clustering regularization term, enabling the known labels and multiple novel labels to be modeled simultaneously.
415	Improving Surveillance Using Cooperative Target Observation	Rashi Aswani, Sai Krishna Munnangi, Praveen Paruchuri	As the fifth variant, we therefore introduce Adjustable Randomization into the best performing observer strategy where the observer can adjust the expected loss in reward due to randomization depending on the situation.
416	Query Complexity of Tournament Solutions	Palash Dey	On the positive side, we are able to circumvent our strong query complexity lower bound results by proving that, if the size of the top cycle of the input tournament is at most k, then we can find all the tournament solutions mentioned above by querying O(nk + n log n / log(1− 1 / k ) ) edges only.
417	Centralized versus Personalized Commitments and Their Influence on Cooperation in Group Interactions	The Anh Han, Luis Moniz Pereira, Luis A. Martinez-Vaquero, Tom Lenaerts	Using methods from Evolutionary Game Theory, this paper shows that, in the context of Public Goods Game, much higher levels of cooperation can be achieved through such centralized commitment management.
418	Kont: Computing Tradeoffs in Normative Multiagent Systems	Ozgur Kafali, Nirav Ajmeri, Munindar P. Singh	We propose Kont, a formal framework for comparing normative multiagent systems (nMASs) by computing tradeoffs among liveness (something good happens) and safety (nothing bad happens).
419	Parameterised Verification of Infinite State Multi-Agent Systems via Predicate Abstraction	Panagiotis Kouvaros, Alessio Lomuscio	We analyse their verification problem by combining and extending existing techniques in parameterised model checking with predicate abstraction procedures.
420	Decentralized Planning in Stochastic Environments with Submodular Rewards	Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar	To address this, we identify models in the cooperative and competitive case that rely on submodular rewards, where we show that existing approximate approaches can provide strong quality guarantees ( a priori, and for cooperative case also posteriori guarantees).
421	Solving Seven Open Problems of Offline and Online Control in Borda Elections	Marc Neveling, Jörg Rothe	We reduce the number of missing cases by pinpointing the complexity of three control scenarios for Borda elections, including some that arguably are among the practically most relevant ones.
422	Collective Multiagent Sequential Decision Making Under Uncertainty	Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau	To address these challenges, we study multiagent planning problems where the collective behavior of a population of agents affects the joint-reward and environment dynamics.
423	Nurturing Group-Beneficial Information-Gathering Behaviors Through Above-Threshold Criteria Setting	Igor Rochlin, David Sarne, Maytal Bremer, Ben Grynhaus	Specifically, the paper provides a comprehensive equilibrium analysis of a threshold-based criteria mechanism for the common cooperative information gathering application, where the criteria is set such that only those whose contribution to the group is above some pre-specified threshold can benefit from the contributions of others.
424	Improving Multi-Document Summarization via Text Classification	Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei	In this paper, we propose a novel summarization system called TCSum, which leverages plentiful text classification data to improve the performance of multi-document summarization.
425	Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions	Guoliang Ji, Kang Liu, Shizhu He, Jun Zhao	In this paper, we propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases.
426	Neural Bag-of-Ngrams	Bofang Li, Tao Liu, Zhe Zhao, Puwei Wang, Xiaoyong Du	In this paper, we introduce the concept of Neural Bag-of-ngrams (Neural-BoN), which replaces sparse one-hot n-gram representation in traditional BoN with dense and rich-semantic n-gram representations.
427	SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents	Ramesh Nallapati, Feifei Zhai, Bowen Zhou	We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
428	Unit Dependency Graph and Its Application to Arithmetic Word Problem Solving	Subhro Roy, Dan Roth	We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework.
429	Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems	Saku Sugawara, Hikaru Yokono, Akiko Aizawa	In order to tackle this problem, we propose in this paper a methodology inspired by unit testing in software engineering that enables the examination of RC systems from multiple aspects. Second, we manually annotate a dataset for an RC task with information regarding the skills needed to answer each question.
430	Neural Machine Translation with Reconstruction	Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, Hang Li	To alleviate this problem, we propose a novel encoder-decoder-reconstructor framework for NMT.
431	SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions	Han Xiao, Minlie Huang, Lian Meng, Xiaoyan Zhu	To this end, this paper proposes the semantic space projection (SSP) model which jointly learns from the symbolic triples and textual descriptions.
432	Efficiently Answering Technical Questions — A Knowledge Graph Approach	Shuo Yang, Lei Zou, Zhongyuan Wang, Jun Yan, Ji-Rong Wen	To improve the online performance, we propose an index-based random walk to support the online search.
433	Incorporating Knowledge Graph Embeddings into Topic Modeling	Liang Yao, Yin Zhang, Baogang Wei, Zhe Jin, Rui Zhang, Yangyang Zhang, Qinfei Chen	In this paper, we propose a novel knowledge-based topic model by incorporating knowledge graph embeddings into topic modeling.
434	A Context-Enriched Neural Network Method for Recognizing Lexical Entailment	Kun Zhang, Enhong Chen, Qi Liu, Chuanren Liu, Guangyi Lv	In this paper, we propose a supervised Context-Enriched Neural Network (CENN) method for recognizing lexical entailment.
435	Bayesian Neural Word Embedding	Oren Barkan	Recently, several works in the domain of natural language processing presented successful methods for word embedding.
436	Improving Word Embeddings with Convolutional Feature Learning and Subword Information	Shaosheng Cao, Wei Lu	We present a novel approach to learning word embeddings by exploring subword information (character n-gram, root/affix and inflections) and capturing the structural information of their context with convolutional feature learning.
437	Joint Copying and Restricted Generation for Paraphrase	Ziqiang Cao, Chuwei Luo, Wenjie Li, Sujian Li	In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.
438	Unsupervised Learning of Evolving Relationships Between Literary Characters	Snigdha Chaturvedi, Mohit Iyyer, Hal Daume III	We present three models based on rich sets of linguistic features that capture various cues about relationships.
439	Translation Prediction with Source Dependency-Based Context Representation	Kehai Chen, Tiejun Zhao, Muyun Yang, Lemao Liu	In this paper, we propose a novel neural network based on bi-convolutional architecture to represent the source dependency-based context for translation prediction.
440	Maximum Reconstruction Estimation for Generative Latent-Variable Models	Yong Cheng, Yang Liu, Wei Xu	We develop tractable algorithms to directly learn hidden Markov models and IBM translation models using the MRE criterion, without the need to introduce a separate reconstruction model to facilitate efficient inference.
441	Incorporating Expert Knowledge into Keyphrase Extraction	Sujatha Das Gollapalli, Xiao-li Li, Peng Yang	We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.
442	Unsupervised Learning for Lexicon-Based Classification	Jacob Eisenstein	This paper describes a set of assumptions that can be used to derive a probabilistic justification for lexicon-based classification, as well as an analysis of its expected accuracy.
443	Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge	Matt Gardner, Jayant Krishnamurthy	Recently proposed methods for open vocabulary semantic parsing overcome this limitation by learning execution models for arbitrary language, essentially using a text corpus as a kind of knowledge base.
444	Geometry of Compositionality	Hongyu Gong, Suma Bhat, Pramod Viswanath	This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way.
445	Disambiguating Spatial Prepositions Using Deep Convolutional Networks	Kaveh Hassani, Won-Sook Lee	We propose a hybrid feature of word embeddings and linguistic features, and compare its performance against a set of linguistic features, pre-trained word embeddings, and corpus-trained embeddings using seven classical machine learning classifiers and two deep learning models.
446	A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media	Hangfeng He, Xu Sun	We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated texts.
447	Recurrent Attentional Topic Model	Shuangyin Li, Yu Zhang, Rong Pan, Mingzhi Mao, Yang Yang	To fill this gap, we propose a Recurrent Attentional Topic Model (RATM) for document embedding.
448	Representations of Context in Recognizing the Figurative and Literal Usages of Idioms	Changsheng Liu, Rebecca Hwa	Developing an appropriate computational model of the context is crucial for automatic idiom usage recognition.
449	Deterministic Attention for Sequence-to-Sequence Constituent Parsing	Chunpeng Ma, Lemao Liu, Akihiro Tamura, Tiejun Zhao, Eiichiro Sumita	In this study, we proposed a deterministic attention mechanism that deterministically selects the important context and is not affected by the sequence length.
450	S2JSD-LSH: A Locality-Sensitive Hashing Schema for Probability Distributions	Xian-Ling Mao, Bo-Si Feng, Yi-Jing Hao, Liqiang Nie, Heyan Huang, Guihua Wen	We define the specific hashing functions, and prove their local-sensitivity.
451	Coherent Dialogue with Attention-Based Language Models	Hongyuan Mei, Mohit Bansal, Matthew R. Walter	We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism.
452	Definition Modeling: Learning to Define Word Embeddings in Natural Language	Thanapon Noraset, Chen Liang, Larry Birnbaum, Doug Downey	In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings’ semantics.
453	Incrementally Learning the Hierarchical Softmax Function for Neural Language Models	Hao Peng, Jianxin Li, Yangqiu Song, Yaopeng Liu	In this paper, we present a training method that can incrementally train the hierarchical softmax function for NNMLs.
454	Condensed Memory Networks for Clinical Diagnostic Inferencing	Aaditya Prakash, Siyuan Zhao, Sadid A. Hasan, Vivek Datla, Kathy Lee, Ashequl Qadir, Joey Liu, Oladimeji Farri	We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory.
455	Robsut Wrod Reocginiton via Semi-Character Recurrent Neural Network	Keisuke Sakaguchi, Kevin Duh, Matt Post, Benjamin Van Durme	Inspired by the findings from the Cmabrigde Uinervtisy effect, we propose a word recognition model based on a semi-character level recurrent neural network (scRNN).
456	Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation	Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron Courville	We introduce a new class of models called multiresolution recurrent neural networks, which explicitly model natural language generation at multiple levels of abstraction.
457	A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues	Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, Yoshua Bengio	To model these dependencies in a generative framework, we propose a neural network-based generative architecture, with stochastic latent variables that span a variable number of time steps.
458	Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation	Jinsong Su, Zhixing Tan, Deyi Xiong, Rongrong Ji, Xiaodong Shi, Yang Liu	Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences.However, for languages without natural word delimiters (e.g., Chinese) where input sentences have to be tokenized first,conventional NMT is confronted with two issues:1) it is difficult to find an optimal tokenization granularity for source sentence modelling, and2) errors in 1-best tokenizations may propagate to the encoder of NMT.To handle these issues, we propose word-lattice based Recurrent Neural Network (RNN) encoders for NMT,which generalize the standard RNN to word lattice topology.The proposed encoders take as input a word lattice that compactly encodes multiple tokenizations, and learn to generate new hidden states from arbitrarily many inputs and hidden states in preceding time steps.As such, the word-lattice based encoders not only alleviate the negative impact of tokenization errors but also are more expressive and flexible to embed input sentences.Experiment results on Chinese-English translation demonstrate the superiorities of the proposed encoders over the conventional encoder.
459	Semantic Parsing with Neural Hybrid Trees	Raymond Hendy Susanto, Wei Lu	We propose a neural graphical model for parsing natural language sentences into their logical representations.
460	Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms	Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao	In this paper, we offer a novel deep learning model, named coupled multi-layer attentions.
461	Dual-Clustering Maximum Entropy with Application to Classification and Word Embedding	Xiaolong Wang, Jingjing Wang, Chengxiang Zhai	To achieve learning efficiency with affordable computational cost, we propose an approach named Dual-Clustering Maximum Entropy (DCME).
462	Neural Machine Translation Advised by Statistical Machine Translation	Xing Wang, Zhengdong Lu, Zhaopeng Tu, Hang Li, Deyi Xiong, Min Zhang	It is natural, therefore, to leverage the advantages of both models for better translations, and in this work we propose to incorporate SMT model into NMT framework.
463	A Dynamic Window Neural Network for CCG Supertagging	Huijia Wu, Jiajun Zhang, Chengqing Zong	We use this approach to demonstrate the state-of-the-art CCG supertagging performance on the standard test set.
464	Distinguish Polarity in Bag-of-Words Visualization	Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, Alok Choudhary	Neural network-based BOW models reveal that word-embedding vectors encode strong semantic regularities.
465	Topic Aware Neural Response Generation	Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, Wei-Ying Ma	To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model.
466	Variational Autoencoder for Semi-Supervised Text Classification	Weidi Xu, Haoze Sun, Chao Deng, Ying Tan	Besides, in order to reduce the computational complexity in training, a novel optimization method is proposed, which estimates the gradient of the unlabeled objective function by sampling, along with two variance reduction techniques.
467	Neural Models for Sequence Chunking	Feifei Zhai, Saloni Potdar, Bing Xiang, Bowen Zhou	In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling.
468	BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings	Biao Zhang, Deyi Xiong, Jinsong Su	In this paper, we propose a bidimensional attention based recursiveautoencoder (BattRAE) to integrate clues and sourcetargetinteractions at multiple levels of granularity into bilingualphrase representations.
469	Bilingual Lexicon Induction from Non-Parallel Data with Minimal Supervision	Meng Zhang, Haoruo Peng, Yang Liu, Huanbo Luan, Maosong Sun	We tackle the limitation by introducing a novel matching mechanism into bilingual word representation learning.
470	Active Discriminative Text Representation Learning	Ye Zhang, Matthew Lease, Byron C. Wallace	We propose a simple approach for sentence classification that selects instances containing words whose embeddings are likely to be updated with the greatest magnitude, thereby rapidly learning discriminative, task-specific embeddings.
471	Learning Context-Specific Word/Character Embeddings	Xiaoqing Zheng, Jiangtao Feng, Yi Chen, Haoyuan Peng, Wenqing Zhang	We present a neural network architecture to jointly learn word embeddings and context representations from large data sets.
472	Mechanism-Aware Neural Machine for Dialogue Response Generation	Ganbin Zhou, Ping Luo, Rongyu Cao, Fen Lin, Bo Chen, Qing He	In this study we propose a mechanism-aware neural machine for dialogue response generation.
473	Bootstrapping Distantly Supervised IE Using Joint Learning and Small Well-Structured Corpora	Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis, Jong Hyuk Park, William W. Cohen	We propose a framework to improve the performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction.
474	Using Discourse Signals for Robust Instructor Intervention Prediction	Muthu Kumar Chandrasekaran, Carrie Demmans Epp, Min-Yen Kan, Diane J. Litman	We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs).
475	Automatic Emphatic Information Extraction from Aligned Acoustic Data and Its Application on Sentence Compression	Yanju Chen, Rong Pan	We introduce a novel method to extract and utilize the semantic information from acoustic data.
476	Unsupervised Sentiment Analysis with Signed Social Networks	Kewei Cheng, Jundong Li, Jiliang Tang, Huan Liu	In this paper, we study a novel problem of unsupervised sentiment analysis with signed social networks.
477	Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction	Ying Ding, Jianfei Yu, Jing Jiang	In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for different domains.
478	Distant Supervision via Prototype-Based Global Representation Learning	Xianpei Han, Le Sun	In this paper, we propose a new DS method — prototype-based global representation learning, which can effectively resolve the multi-instance problem and the missing label problem by learning informative entity pair representations, and building discriminative extraction models at the entity pair level, rather than at the instance level.
479	What Happens Next? Future Subevent Prediction Using Contextual Hierarchical LSTM	Linmei Hu, Juanzi Li, Liqiang Nie, Xiao-Li Li, Chao Shao	In this paper, we develop an end-to-end model which directly takes the texts describing previous subevents as input and automatically generates a short text describing a possible future subevent.
480	Efficient Dependency-Guided Named Entity Recognition	Zhanming Jie, Aldrian Obaja Muis, Wei Lu	In this work, we investigate on how to better utilize the structured information conveyed by dependency trees to improve the performance of NER.
481	Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks	Canasai Kruengkrai, Kentaro Torisawa, Chikara Hashimoto, Julien Kloetzer, Jong-Hoon Oh, Masahiro Tanaka	We propose a method for recognizing such event causalities as “smoke cigarettes” → “die of lung cancer” using background knowledge taken from web texts as well as original sentences from which candidates for the causalities were extracted.
482	Efficiently Mining High Quality Phrases from Texts	Bing Li, Xiaochun Yang, Bin Wang, Wei Cui	In this paper, we propose an efficient high-quality phrase mining approach (EQPM).
483	Learning Latent Sentiment Scopes for Entity-Level Sentiment Analysis	Hao Li, Wei Lu	In this paper, we focus on the task of extracting named entities together with their associated sentiment information in a joint manner.
484	Structural Correspondence Learning for Cross-Lingual Sentiment Classification with One-to-Many Mappings	Nana Li, Shuangfei Zhai, Zhongfei Zhang, Boying Liu	In this paper, we propose a cross-lingual SCL based on distributed representation of words; it can learn meaningful one-to-many mappings for pivot words using large amounts of monolingual data and a small dictionary.
485	Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization	Piji Li, Zihao Wang, Wai Lam, Zhaochun Ren, Lidong Bing	We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation.
486	Collaborative User Clustering for Short Text Streams	Shangsong Liang, Zhaochun Ren, Emine Yilmaz, Evangelos Kanoulas	In this paper, we study the problem of user clustering in the context of their published short text streams.
487	Word Embedding Based Correlation Model for Question/Answer Matching	Yikang Shen, Wenge Rong, Nan Jiang, Baolin Peng, Jie Tang, Zhang Xiong	In this paper, a Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding.
488	Greedy Flipping for Constrained Word Deletion	Jin-ge Yao, Xiaojun Wan	In this paper we propose a simple yet efficient method for constrained word deletion to compress sentences, based on top-down greedy local flipping from multiple random initializations.
489	Attentive Interactive Neural Networks for Answer Selection in Community Question Answering	Xiaodong Zhang, Sujian Li, Lei Sha, Houfeng Wang	In this paper, we propose to treat different text segments differently and design a novel attentive interactive neural network (AI-NN) to focus on those text segments useful to answer selection.
490	Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning	Zhou Zhao, Hanqing Lu, Vincent W. Zheng, Deng Cai, Xiaofei He, Yueting Zhuang	In this paper, we consider the problem of community-based question answering from the viewpoint of asymmetric multi-faceted ranking network embedding.
491	Plan Reordering and Parallel Execution — A Parameterized Complexity View	Meysam Aghighi, Christer Bäckström	We revisit these problems, but applying parameterized complexity analysis rather than standard complexity analysis.
492	Validating Domains and Plans for Temporal Planning via Encoding into Infinite-State Linear Temporal Logic	Alessandro Cimatti, Andrea Micheli, Marco Roveri	In this paper, we present a technique to simplify the creation ofcorrect models by leveraging formal-verification tools for automaticvalidation.
493	On the Disruptive Effectiveness of Automated Planning for LTL	Giuseppe De Giacomo, Fabrizio Maria Maggi, Andrea Marrella, Fabio Patrizi	We provide a sound and complete technique to synthesize the alignment instructions relying on finite automata theoretic manipulations.
494	Bounding the Probability of Resource Constraint Violations in Multi-Agent MDPs	Frits de Nijs, Erwin Walraven, Mathijs M. de Weerdt, Matthijs T. J. Spaan	We derive a method to bound constraint violation probabilities using Hoeffding’s inequality.
495	Optimizing Quantiles in Preference-Based Markov Decision Processes	Hugo Gilbert, Paul Weng, Yan Xu	As this decision criterion is not always suitable, we propose in this paper an algorithm for computing a policy optimal for the quantile criterion.
496	An Analysis of Monte Carlo Tree Search	Steven James, George Konidaris, Benjamin Rosman	We present experimental evidence suggesting that, under certain smoothness conditions, uniformly random simulation policies preserve the ordering over action preferences.
497	An Efficient Approach to Model-Based Hierarchical Reinforcement Learning	Zhuoru Li, Akshay Narayan, Tze-Yun Leong	We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems.
498	Best-First Width Search: Exploration and Exploitation in Classical Planning	Nir Lipovetzky, Hector Geffner	In this work, we address this problem but using structural exploration methods resulting from the ideas of width-based search.
499	Robust Execution of Probabilistic Temporal Plans	Kyle Lund, Sam Dietrich, Scott Chow, James C. Boerkoel	This paper introduces the Robust Execution Problem for finding maximally robust dispatch strategies for general probabilistic temporal planning problems.
500	Multi-Agent Path Finding with Delay Probabilities	Hang Ma, T. K. Satish Kumar, Sven Koenig	Multi-Agent Path Finding with Delay Probabilities
501	Logical Filtering and Smoothing: State Estimation in Partially Observable Domains	Brent Mombourquette, Christian Muise, Sheila A. McIlraith	We propose logical smoothing, a form of backwards reasoning that works in concert with approximated logical filtering to refine past beliefs in light of new observations.
502	Landmark-Based Heuristics for Goal Recognition	Ramon Fraga Pereira, Nir Oren, Felipe Meneguzzi	In this paper, we propose goal recognition heuristics that rely on information from planning landmarks – facts or actions that must occur if a plan is to achieve a goal when starting from some initial state.
503	Fast SSP Solvers Using Short-Sighted Labeling	Luis Enrique Pineda, Kyle Hollins Wray, Shlomo Zilberstein	Based on this idea, we propose the FLARES algorithm and show that it performs consistently well on a wide range of benchmark problems.
504	Higher-Dimensional Potential Heuristics for Optimal Classical Planning	Florian Pommerening, Malte Helmert, Blai Bonet	Previous work showed that the set of all admissible and consistent potential heuristics usingatomic features can be characterized by a compact set of linear constraints.
505	Schematic Invariants by Reduction to Ground Invariants	Jussi Rintanen	We propose algorithms that reduce the problem of finding schematic invariants to solving a smaller ground problem.
506	Narrowing the Gap Between Saturated and Optimal Cost Partitioning for Classical Planning	Jendrik Seipp, Thomas Keller, Malte Helmert	We show that searching in the space of orders leads to significantly better heuristic estimates than with previously considered orders.
507	Incorporating Domain-Independent Planning Heuristics in Hierarchical Planning	Vikas Shivashankar, Ron Alford, David W. Aha	To address this, we present a principled framework for incorporating DIP heuristics into HGN planning using a simple relaxation of the HGN semantics we call Hierarchy-Relaxation.
508	Computational Issues in Time-Inconsistent Planning	Pingzhong Tang, Yifeng Teng, Zihe Wang, Shenke Xiao, Yichong Xu	In this paper, we give answers to all three open problems.
509	Accelerated Vector Pruning for Optimal POMDP Solvers	Erwin Walraven, Matthijs T. J. Spaan	In this paper we show how the LPs in POMDP pruning subroutines can be decomposed using a Benders decomposition.
510	When to Reset Your Keys: Optimal Timing of Security Updates via Learning	Zizhan Zheng, Ness B. Shroff, Prasant Mohapatra	In this work, we make an initial effort towards achieving optimal timing of security updates in the face of unknown stealthy attacks.
511	Human-Aware Plan Recognition	Hankz Hankui Zhuo	In the experiment, we evaluate our approach in three planning domains to demonstrate its effectiveness.
512	Minimal Undefinedness for Fuzzy Answer Sets	Mario Alviano, Giovanni Amendola, Rafael Peñaloza	The aim of this paper is to enforce such a principle in FASP through the minimization of a measure of undefinedness.
513	Open-Universe Weighted Model Counting	Vaishak Belle	In this paper, we revisit relational probabilistic models over an infinite domain, and establish a number of results that permit effective algorithms.
514	Deterministic versus Probabilistic Methods for Searching for an Evasive Target	Sara Bernardini, Maria Fox, Derek Long, Chiara Piacentini	In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i.e., a target that is particularly difficult to track down and uncertain to locate.
515	Non-Deterministic Planning with Temporally Extended Goals: LTL over Finite and Infinite Traces	Alberto Camacho, Eleni Triantafillou, Christian Muise, Jorge A. Baier, Sheila A. McIlraith	We propose several different compilations based on translations of LTL to (Büchi) alternating or (Büchi) non-deterministic finite state automata, and evaluate various properties of the competing approaches.
516	Optimizing Expectation with Guarantees in POMDPs	Krishnendu Chatterjee, Petr Novotný, Guillermo A. Pérez, Jean-François Raskin, Đorđe Žikelić	In this work we go beyond both the “expectation” and “threshold” approaches and consider a “guaranteed payoff optimization (GPO)” problem for POMDPs, where we are given a threshold t and the objective is to find a policy σ such that a) each possible outcome of σ yields a discounted-sum payoff of at least t, and b) the expected discounted-sum payoff of σ is optimal (or near-optimal) among all policies satisfying a).
517	Latent Dependency Forest Models	Shanbo Chu, Yong Jiang, Kewei Tu	In this paper, we propose a novel type of probabilistic models named latent dependency forest models (LDFMs).
518	Causal Effect Identification by Adjustment under Confounding and Selection Biases	Juan D. Correa, Elias Bareinboim	In this paper, we introduce a generalized version of covariate adjustment that simultaneously controls for both confounding and selection biases.
519	The Linearization of Belief Propagation on Pairwise Markov Random Fields	Wolfgang Gatterbauer	For the case when all edges in the MRF carry the same symmetric, doubly stochastic potential, recent works have proposed to approximate BP by linearizing the update equations around default values, which was shown to work well for the problem of node classification.
520	The Kernel Kalman Rule — Efficient Nonparametric Inference with Recursive Least Squares	Gregor H. W. Gebhardt, Andras Kupcsik, Gerhard Neumann	In this paper, we present the kernel Kalman rule (KKR) as an alternative to the KBR.The derivation of the KKR is based on recursive least squares, inspired by the derivation of the Kalman innovation update.We apply the KKR to filtering tasks where we use RKHS embeddings to represent the belief state, resulting in the kernel Kalman filter (KKF).
521	Misspecified Linear Bandits	Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan	We consider the problem of online learning in misspecified linear stochastic multi-armed bandit problems.
522	Reasoning about Cognitive Trust in Stochastic Multiagent Systems	Xiaowei Huang, Marta Zofia Kwiatkowska	We propose a probabilistic rational temporal logic PRTL, which extends the logic PCTL with reasoning about mental attitudes (beliefs, goals and intentions), and includes novel operators that can express concepts of social trust such as competence, disposition and dependence.
523	Anytime Best+Depth-First Search for Bounding Marginal MAP	Radu Marinescu, Junkyu Lee, Alexander Ihler, Rina Dechter	We introduce new anytime search algorithms that combine best-first with depth-first search into hybrid schemes for Marginal MAP inference in graphical models.
524	Multi-Objective Influence Diagrams with Possibly Optimal Policies	Radu Marinescu, Abdul Razak, Nic Wilson	In this paper, we consider alternative notions of optimality, one of the most important one being the notion of possibly optimal, namely optimal in at least one scenario compatible with the inter-objective tradeoffs.
525	Hindsight Optimization for Hybrid State and Action MDPs	Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern	Our main contribution is a linear time reduction to a Mixed Integer Linear Program (MILP) that encodes the HOP objective, when the dynamics are specified as location-scale probability distributions parametrized by Piecewise Linear (PWL) functions of states and actions.
526	I See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior	Felix Schmitt, Hans-Joachim Bieg, Michael Herman, Constantin A. Rothkopf	To the best of our knowledge, this work presents the first general approach for joint inference of sensor and policy models.
527	Solving Constrained Combinatorial Optimisation Problems via MAP Inference without High-Order Penalties	Zhen Zhang, Qinfeng Shi, Julian McAuley, Wei Wei, Yanning Zhang, Rui Yao, Anton van den Hengel	We propose an approach which is able to solve constrained combinatorial problems using belief propagation without increasing the order.
528	Deep Learning Quadcopter Control via Risk-Aware Active Learning	Olov Andersson, Mariusz Wzorek, Patrick Doherty	In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter.
529	Latent Dirichlet Allocation for Unsupervised Activity Analysis on an Autonomous Mobile Robot	Paul Duckworth, Muhannad Alomari, James Charles, David C. Hogg, Anthony G. Cohn	We present a method for unsupervised learning of common human movements and activities on an autonomous mobile robot, which generalises and improves on recent results.
530	Unsupervised Feature Learning for 3D Scene Reconstruction with Occupancy Maps	Vitor Campanholo Guizilini, Fabio Tozeto Ramos	This paper addresses the task of unsupervised feature learning for three-dimensional occupancy mapping, as a way to segment higher-level structures based on raw unorganized point cloud data.
531	Grounded Action Transformation for Robot Learning in Simulation	Josiah P. Hanna, Peter Stone	This paper proposes a new algorithm for GSL — Grounded Action Transformation — and applies it to learning of humanoid bipedal locomotion.
532	A Diversified Generative Latent Variable Model for WiFi-SLAM	Hao Xiong, Dacheng Tao	Here we propose the diversified generative latent variable model (DGLVM) to overcome these limitations.
533	Associate Latent Encodings in Learning from Demonstrations	Hang Yin, Francisco S. Melo, Aude Billard, Ana Paiva	We contribute a learning from demonstration approach for robots to acquire skills from multi-modal high-dimensional data.
534	Dynamically Constructed (PO)MDPs for Adaptive Robot Planning	Shiqi Zhang, Piyush Khandelwal, Peter Stone	In this paper, we present a novel algorithm called iCORPP to dynamically reason about, and construct (PO)MDPs using P-LOG.
535	A SAT-Based Approach for Solving the Modal Logic S5-Satisfiability Problem	Thomas Caridroit, Jean-Marie Lagniez, Daniel Le Berre, Tiago de Lima, Valentin Montmirail	We present a SAT-based approach for solving the modal logic S5-satisfiability problem.
536	A BTP-Based Family of Variable Elimination Rules for Binary CSPs	Achref El Mouelhi	Here, we introduce a new weaker form of BTP, which will be calledm-fBTP for flexible broken-triangle property.m-fBTP allows on the one hand to eliminate more variables than BTP while preserving satisfiability and on the other to define new bigger tractable class for which arc consistency is a decision procedure.
537	Algorithms for Deciding Counting Quantifiers over Unary Predicates	Marcelo Finger, Glauber De Bona	We propose a fragment over unary predicates that is NP-complete and for which there is a nor- mal form where Counting Quantification sentences have a single Unary predicate, thus call it the CQU fragment.
538	Maximum Model Counting	Daniel J. Fremont, Markus N. Rabe, Sanjit A. Seshia	We introduce the problem Max#SAT, an extension of model counting (#SAT).
539	Phase Transitions for Scale-Free SAT Formulas	Tobias Friedrich, Anton Krohmer, Ralf Rothenberger, Andrew M. Sutton	For scale-free formulas with clauses of length k=2, we give a lower bound on the phase transition threshold as a function of the scaling parameter.
540	The Opacity of Backbones	Lane A. Hemaspaandra, David E. Narváez	This paper studies the nontransparency of backbones.
541	Between Subgraph Isomorphism and Maximum Common Subgraph	Ruth Hoffmann, Ciaran McCreesh, Craig Reilly	We introduce a restricted alternative, where we ask if all but k vertices from the pattern can be found in the target graph.
542	Should Algorithms for Random SAT and Max-SAT Be Different?	Sixue Liu, Gerard de Melo	In light of these results, we propose a novel probabilistic approach for random Max-SAT called ProMS.
543	Soft and Cost MDD Propagators	Guillaume Perez, Jean-Charles Régin	In this paper, we take another step in this direction by improving the propagators of cost MDDs.
544	Rigging Nearly Acyclic Tournaments Is Fixed-Parameter Tractable	M. S. Ramanujan, Stefan Szeider	In this paper we study the algorithmic problem of manipulating the outcome of a tournament.
545	RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem	Saeedeh Shekarpour, Edgard Marx, Sören Auer, Amit Sheth	For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system.However, there is a risk of receiving queries which do not match with the background knowledge.Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy.In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases.We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources.We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data.This model was bootstrapped with three statistical distributions.Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.
546	CoCoA: A Non-Iterative Approach to a Local Search (A)DCOP Solver	Cornelis Jan van Leeuwen, Przemyslaw Pawelczak	We propose a novel incomplete cooperative algorithm for distributed constraint optimization problems (DCOPs) denoted as Cooperative Constraint Approximation (CoCoA).
547	Extending Compact-Table to Negative and Short Tables	Hélène Verhaeghe, Christophe Lecoutre, Pierre Schaus	In this paper, we extend this algorithm in order to deal with both short supports and negative tables, i.e., tables that contain universal values and conflicts.
548	General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis	Colin Wei, Stefano Ermon	In this paper, we present new bounds for the location of these thresholds in boolean CSPs.
549	Regularized Diffusion Process for Visual Retrieval	Song Bai, Xiang Bai, Qi Tian, Longin Jan Latecki	In this paper, we propose a new variant o diffusion process, which also operates on a tensor product graph.
550	Collective Deep Quantization for Efficient Cross-Modal Retrieval	Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu	This paper presents a compact coding solution for efficient cross-modal retrieval, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in single-modal similarity retrieval.
551	Reference Based LSTM for Image Captioning	Minghai Chen, Guiguang Ding, Sicheng Zhao, Hui Chen, Qiang Liu, Jungong Han	In this paper, we consider the training images as the references and propose a Reference based Long Short Term Memory (R-LSTM) model, aiming to solve these two problems in one goal.
552	A Multi-Task Deep Network for Person Re-Identification	Weihua Chen, Xiaotang Chen, Jianguo Zhang, Kaiqi Huang	In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID.
553	VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem	Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni	In this paper we present an on-manifold sequence-to-sequence learning approach to motion estimation using visual and inertial sensors.
554	Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval	Guoxian Dai, Jin Xie, Fan Zhu, Yi Fang	In the paper, we propose a novel deep correlated metric learning (DCML) method to mitigate the discrepancy between sketch and 3D shape domains.
555	Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition	Zhen Dong, Su Jia, Chi Zhang, Mingtao Pei, Yuwei Wu	In this paper, we aim to construct a deep neural network which embeds high dimensional symmetric positive definite (SPD) matrices into a more discriminative low dimensional SPD manifold.
556	Sherlock: Scalable Fact Learning in Images	Mohamed Elhoseiny, Scott Cohen, Walter Chang, Brian Price, Ahmed Elgammal	We propose a setting where all these facts can be modeled simultaneously with a capacity to understand an unbounded number of facts in a structured way.
557	Robust Visual Tracking via Local-Global Correlation Filter	Heng Fan, Jinhai Xiang	To address this problem, we propose a novel local-global correlation filter (LGCF) for object tracking.
558	DECK: Discovering Event Composition Knowledge from Web Images for Zero-Shot Event Detection and Recounting in Videos	Chuang Gan, Chen Sun, Ram Nevatia	In this paper, we provide a fully automatic algorithm to select representative and reliable concepts for event queries.
559	Differentiating Between Posed and Spontaneous Expressions with Latent Regression Bayesian Network	Quan Gan, Siqi Nie, Shangfei Wang, Qiang Ji	To tackle this problem, we present a generative model, i.e., Latent Regression Bayesian Network (LRBN), to effectively capture the spatial patterns embedded in facial landmark points to differentiate between posed and spontaneous facial expressions.
560	Active Video Summarization: Customized Summaries via On-line Interaction with the User	Ana Garcia del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan	In this paper, we introduce Active Video Summarization (AVS), an interactive approach to gather the user’s preferences while creating the summary. We also introduce a new dataset for customized video summarization (CSumm) recorded with a Google Glass.
561	Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution	Jun Guo, Hongyang Chao	We propose an end-to-end deep network for video super-resolution.
562	Zero-Shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels	Yuchen Guo, Guiguang Ding, Jungong Han, Yue Gao	Rather than following this two-step strategy, in this paper, we propose a novel one-step approach that is able to perform ZSR in the original feature space by using directly trained classifiers.
563	Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification	Emily M. Hand, Rama Chellappa	We propose a multi-task deep convolutional neural network (MCNN) with an auxiliary network at the top (AUX) which takes advantage of attribute relationships for improved classification.
564	Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification	Xiangteng He, Yuxin Peng	Therefore, this paper proposes a weakly supervised part selection method with spatial constraints for fine-grained image classification, which is free of using any bounding box or part annotations.
565	Video Recovery via Learning Variation and Consistency of Images	Zhouyuan Huo, Shangqian Gao, Weidong Cai, Heng Huang	To solve this problem, we propose a new video recovery method Sectional Trace Norm with Variation and Consistency Constraints (STN-VCC).
566	Nonnegative Orthogonal Graph Matching	Bo Jiang, Jin Tang, Chris Ding, Bin Luo	In this paper, we propose a new algorithm, called Nonnegative Orthogonal Graph Matching (NOGM), for QAP matching problem.
567	Multi-Path Feedback Recurrent Neural Networks for Scene Parsing	Xiaojie Jin, Yunpeng Chen, Zequn Jie, Jiashi Feng, Shuicheng Yan	In this paper, we consider the scene parsing problem and propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene images.
568	Detection and Recognition of Text Embedded in Online Images via Neural Context Models	Chulmoo Kang, Gunhee Kim, Suk I. Yoo	Our idea is to leverage context information for both text detection and recognition.
569	Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network	Suha Kwak, Seunghoon Hong, Bohyung Han	We propose a weakly supervised semantic segmentation algorithm based on deep neural networks, which relies on image-level class labels only.
570	Robust MIL-Based Feature Template Learning for Object Tracking	Xiangyuan Lan, Pong C. Yuen, Rama Chellappa	To address these issues, this paper proposes a novel and optimal model learning (updating) scheme which aims to simultaneously eliminate the negative effects from these two issues mentioned above in a unified robust feature template learning framework.
571	Learning Patch-Based Dynamic Graph for Visual Tracking	Chenglong Li, Liang Lin, Wangmeng Zuo, Jin Tang	To handle this problem, we aim to learn a more robust object representation for visual tracking.
572	Image Caption with Global-Local Attention	Linghui Li, Sheng Tang, Lixi Deng, Yongdong Zhang, Qi Tian	To address these problems, in this paper, we propose a global-local attention (GLA) method by integrating local representation at object-level with global representation at image-level through attention mechanism.
573	Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models	Siyi Li, Dit-Yan Yeung	In this paper, we consider one such scenario in which the camera is mounted on an unmanned aerial vehicle (UAV) or drone. We build a benchmark dataset of high diversity, consisting of 70 videos captured by drone cameras.
574	A Multiview-Based Parameter Free Framework for Group Detection	Xuelong Li, Mulin Chen, Feiping Nie, Qi Wang	In this paper,we propose the Multiview-based Parameter Free (MPF) approach to detect groups in crowd scenes.
575	Weakly-Supervised Deep Nonnegative Low-Rank Model for Social Image Tag Refinement and Assignment	Zechao Li, Jinhui Tang	To alleviate this problem, we propose a Weakly-supervised Deep Nonnegative Low-rank model (WDNL) to improve the quality of tags by integrating the low-rank model with deep feature learning.
576	TextBoxes: A Fast Text Detector with a Single Deep Neural Network	Minghui Liao, Baoguang Shi, Xiang Bai, Xinggang Wang, Wenyu Liu	This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression.
577	An Artificial Agent for Robust Image Registration	Rui Liao, Shun Miao, Pierre de Tournemire, Sasa Grbic, Ali Kamen, Tommaso Mansi, Dorin Comaniciu	In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task.
578	Attention Correctness in Neural Image Captioning	Chenxi Liu, Junhua Mao, Fei Sha, Alan Yuille	In this paper we focus on evaluating and improving the correctness of attention in neural image captioning models.
579	Boosting Complementary Hash Tables for Fast Nearest Neighbor Search	Xianglong Liu, Cheng Deng, Yadong Mu, Zhujin Li	To address the problem, this paper proposes a multi-table learning method which pursues a specified number of complementary and informative hash tables from a perspective of ensemble learning.
580	Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition	Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, Yuanqing Lin	Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
581	Video Captioning with Listwise Supervision	Yuan Liu, Xue Li, Zhongchao Shi	In this paper, we propose to model relative relationships of different video-sentence pairs and present a novel framework, named Long Short-Term Memory with Listwise Supervision (LSTM-LS), for video captioning.
582	Closing the Loop for Edge Detection and Object Proposals	Yao Lu, Linda Shapiro	In this paper, we argue that edge detection and object proposals should benefit one another.
583	Learning Discriminative Activated Simplices for Action Recognition	Chenxu Luo, Chang Ma, Chunyu Wang, Yizhou Wang	To solve the problems, we propose a novel representation for 3D poses by a mixture of Discriminative Activated Simplices (DAS).
584	Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization	Jiayi Ma, Ji Zhao, Junjun Jiang, Huabing Zhou	In this paper, we propose a robust transformation estimation method based on manifold regularization for non-rigid point set registration.
585	Online Multi-Target Tracking Using Recurrent Neural Networks	Anton Milan, S. Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler	We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs).
586	Text-Guided Attention Model for Image Captioning	Jonghwan Mun, Minsu Cho, Bohyung Han	Inspired by this, we introduce a text-guided attention model for image captioning, which learns to drive visual attention using associated captions.
587	Fully Convolutional Neural Networks with Full-Scale-Features for Semantic Segmentation	Tianxiang Pan, Bin Wang, Guiguang Ding, Jun-Hai Yong	In this work, we propose a novel method to involve full-scale-features into the fully convolutional neural networks (FCNs) for Semantic Segmentation.
588	Title Learning Latent Subevents in Activity Videos Using Temporal Attention Filters	A. J. Piergiovanni, Chenyou Fan, Michael S. Ryoo	In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos.
589	Privacy-Preserving Human Activity Recognition from Extreme Low Resolution	Michael S. Ryoo, Brandon Rothrock, Charles Fleming, Hyun Jong Yang	This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16×12) anonymized videos.
590	An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data	Sijie Song, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jiaying Liu	In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data.
591	Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs	Xinhang Song, Luis Herranz, Shuqiang Jiang	In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images.
592	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander A Alemi	Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly.
593	Image Cosegmentation via Saliency-Guided Constrained Clustering with Cosine Similarity	Zhiqiang Tao, Hongfu Liu, Huazhu Fu, Yun Fu	In this paper, a novel clustering algorithm, called Saliency-Guided Constrained Clustering approach with Cosine similarity (SGC3), is proposed for the image cosegmentation task, where the common foregrounds are extracted via a one-step clustering process.
594	Quantifying and Detecting Collective Motion by Manifold Learning	Qi Wang, Mulin Chen, Xuelong Li	By investigating the similarity of individuals, this paper proposes a novel framework for both quantifying and detecting collective motions.
595	Cross-View People Tracking by Scene-Centered Spatio-Temporal Parsing	Yuanlu Xu, Xiaobai Liu, Lei Qin, Song-Chun Zhu	In this paper, we propose a Spatio-temporal Attributed Parse Graph (ST-APG) to integrate semantic attributes with trajectories for cross-view people tracking. The inference is solved by iteratively grouping tracklets with cluster sampling and estimating people semantic attributes by dynamic programming.In experiments, we validate our method on one public dataset and create another new dataset that records people’s daily life in public, e.g., food court, office reception and plaza, each of which includes 3-4 cameras.
596	Unsupervised Learning of Multi-Level Descriptors for Person Re-Identification	Yang Yang, Longyin Wen, Siwei Lyu, Stan Z. Li	In this paper, we propose a novel coding method named weighted linear coding (WLC) to learn multi-level (e.g., pixel-level, patch-level and image-level) descriptors from raw pixel data in an unsupervised manner.
597	Leveraging Saccades to Learn Smooth Pursuit: A Self-Organizing Motion Tracking Model Using Restricted Boltzmann Machines	Arjun Yogeswaran, Pierre Payeur	In this paper, we propose a biologically-plausible model to explain the emergence of motion tracking behaviour in early development using unsupervised learning.
598	Efficient Object Instance Search Using Fuzzy Objects Matching	Tan Yu, Yuwei Wu, Sreyasee Bhattacharjee, Junsong Yuan	In this paper, we propose a Fuzzy Objects Matching (FOM) framework to effectively and efficiently capture the relevance between the query object and reference images in the dataset.
599	Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks	Xin Yu, Fatih Porikli	To overcome this challenge, here we present an end-to-end transformative discriminative neural network (TDN) devised for super-resolving unaligned and very small face images with an extreme upscaling factor of 8.
600	Leveraging Video Descriptions to Learn Video Question Answering	Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles, Min Sun	We propose a scalable approach to learn video-based question answering (QA): to answer a free-form natural language question about the contents of a video.
601	Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image	Xiaoke Zhu, Xiao-Yuan Jing, Fei Wu, Yunhong Wang, Wangmeng Zuo, Wei-Shi Zheng	In this paper, we propose a joint feature projection matrix and heterogeneous dictionary pair learning (PHDL) approach for IVPR.
602	Natural Language Acquisition and Grounding for Embodied Robotic Systems	Muhannad Alomari, Paul Duckworth, David C. Hogg, Anthony G. Cohn	We present a cognitively plausible novel framework capable of learning the grounding in visual semantics and the grammar of natural language commands given to a robot in a table top environment.
603	Analogical Chaining with Natural Language Instruction for Commonsense Reasoning	Joseph A. Blass, Kenneth D. Forbus	This paper describes analogical chaining, natural language instruction via microstories, and some subtleties that arise in controlling reasoning.
604	Inductive Reasoning about Ontologies Using Conceptual Spaces	Zied Bouraoui, Shoaib Jameel, Steven Schockaert	In this paper, we propose a new method for knowledge base completion, which uses interpretable conceptual space representations and an explicit model for inductive inference that is closer to human forms of commonsense reasoning.
605	Integrating the Cognitive with the Physical: Musical Path Planning for an Improvising Robot	Mason Bretan, Gil Weinberg	In this work, a proof of concept demonstrating the utility of an embodied musical cognition for robotic musicianship is described.
606	Imagined Visual Representations as Multimodal Embeddings	Guillem Collell, Ted Zhang, Marie-Francine Moens	In this paper, we present a simple and effective method that learns a language-to-vision mapping and uses its output visual predictions to build multimodal representations.
607	Goal Operations for Cognitive Systems	Michael T. Cox, Dustin Dannenhauer, Sravya Kondrakunta	We introduce goal transformation at the metacognitive level as well as goal transformation in an automated planner and discuss the costs and benefits of each approach.
608	Combining Logical Abduction and Statistical Induction: Discovering Written Primitives with Human Knowledge	Wang-Zhou Dai, Zhi-Hua Zhou	In this paper, we propose an approach, LASIN, which combines Logical Abduction and Statistical Induction.
609	Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game	Elias Fernández Domingos, Juan Carlos Burguillo, Tom Lenaerts	In this paper, we evaluate whether this conclusion extends also to gift-giving games, more concretely, to a game that combines the dictator game with a partner selection process.
610	Towards Continuous Scientific Data Analysis and Hypothesis Evolution	Yolanda Gil, Daniel Garijo, Varun Ratnakar, Rajiv Mayani, Ravali Adusumilli, Hunter Boyce, Arunima Srivastava, Parag Mallick	We present a framework for automated discovery from data repositories that tests user-provided hypotheses using expert-grade data analysis strategies, and reassesses hypotheses when more data becomes available.
611	Flexible Model Induction through Heuristic Process Discovery	Pat Langley, Adam Arvay	In this paper, we present a more flexible approach that, when available processes are insufficient to construct an acceptable model, automatically produces new generic processes that let it complete the task.
612	When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?	Smitha Milli, Falk Lieder, Thomas L. Griffiths	We investigate these questions in two settings: a one-shot decision between two alternatives, and planning under uncertainty in a Markov decision process.
613	Scanpath Complexity: Modeling Reading Effort Using Gaze Information	Abhijit Mishra, Diptesh Kanojia, Seema Nagar, Kuntal Dey, Pushpak Bhattacharyya	We propose a quantification of reading effort by measuring the complexity of eye-movement patterns of readers.
614	Identifying Useful Inference Paths in Large Commonsense Knowledge Bases by Retrograde Analysis	Abhishek Sharma, Keith M. Goolsbey	In this paper, we use retrograde analysis to build a database of proof paths that lead to at least one successful proof.
615	ConceptNet 5.5: An Open Multilingual Graph of General Knowledge	Robert Speer, Joshua Chin, Catherine Havasi	We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings.
616	Towards a Brain Inspired Model of Self-Awareness for Sociable Agents	Budhitama Subagdja, Ah-Hwee Tan	In moving towards that direction, in this paper, a brain inspired model of self-awareness is presented that allows an agent to learn to attend to different aspects of self as an individual with identity, physical embodiment, mental states, experiences, and reflections on how others may think about oneself.
617	Semantic Proto-Role Labeling	Adam Teichert, Adam Poliak, Benjamin Van Durme, Matthew R. Gormley	We approach proto-role labeling as a multi-label classification problem and establish strong results for the task by adapting a successful model of traditional semantic role labeling.
618	Matrix Factorisation for Scalable Energy Breakdown	Nipun Batra, Hongning Wang, Amarjeet Singh, Kamin Whitehouse	In this paper, we propose a novel application of feature-based matrix factorisation that does not require any additional hard- ware installation.
619	Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data	Souhaib Ben Taieb, Jiafan Yu, Mateus Neves Barreto, Ram Rajagopal	In order to provide more robustness to estimation errors in the adjustments, we present a new hierarchical forecasting algorithm that computes sparse adjustments while still preserving the aggregation constraints.
620	Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method	Zhiguang Cao, Hongliang Guo, Jie Zhang, Frans Oliehoek, Ulrich Fastenrath	We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy.
621	Counting-Based Reliability Estimation for Power-Transmission Grids	Leonardo Duenas-Osorio, Kuldeep S. Meel, Roger Paredes, Moshe Y. Vardi	In this paper, we investigate how recent advances in hashing-based approaches to counting can be exploited to improve computational techniques for system reliability.The primary contribution of this paper is a novel framework, RelNet, that reduces the problem of computing reliability for a given network to counting the number of satisfying assignments of a Σ11 formula, which is amenable to recent hashing-based techniques developed for counting satisfying assignments of SAT formula.
622	Three New Algorithms to Solve N-POMDPs	Yann Dujardin, Tom Dietterich, Iadine Chadès	This paper proposes three new algorithms, based on a general approach that we call alpha-min-2.
623	Fine-Grained Car Detection for Visual Census Estimation	Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Li Fei-Fei	In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences.
624	Spatial Projection of Multiple Climate Variables Using Hierarchical Multitask Learning	Andre R. Goncalves, Arindam Banerjee, Fernando J. Von Zuben	In this paper we introduce a framework for hierarchical multitask learning (HMTL) with two levels of tasks such that each super-task, i.e., task at the top level, is itself a multitask learning problem over sub-tasks.
625	Species Distribution Modeling of Citizen Science Data as a Classification Problem with Class-Conditional Noise	Rebecca A. Hutchinson, Liqiang He, Sarah C. Emerson	In this paper, we propose to formulate the species distribution modeling task as a classification problem with class-conditional noise.
626	Combining Satellite Imagery and Open Data to Map Road Safety	Alameen Najjar, Shun’ichi Kaneko, Yoshikazu Miyanaga	To this end, we propose a deep learning-based mapping approach that leverages open data to learn from raw satellite imagery robust deep models able to predict accurate city-scale road safety maps at an affordable cost.
627	Fast-Tracking Stationary MOMDPs for Adaptive Management Problems	Martin Péron, Kai Helge Becker, Peter Bartlett, Iadine Chadès	Under the assumption (common in adaptive management) that the true transition matrix is stationary, we propose a polynomial-time algorithm to find a lower bound of the value function.
628	Extracting Urban Microclimates from Electricity Bills	Thuy Vu, D. Stott Parker	Extracting Urban Microclimates from Electricity Bills
629	Robust Optimization for Tree-Structured Stochastic Network Design	Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein	We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers.
630	Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information	Yexiang Xue, Xiaojian Wu, Dana Morin, Bistra Dilkina, Angela Fuller, J. Andrew Royle, Carla P. Gomes	We propose a novel approach to dynamically optimize landscape connectivity.
631	Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data	Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon	We introduce a scalable, accurate, and inexpensive method to predict crop yields using publicly available remote sensing data.
632	Healthy Cognitive Aging: A Hybrid Random Vector Functional-Link Model for the Analysis of Alzheimer’s Disease	Peng Dai, Femida Gwadry-Sridhar, Michael Bauer, Michael Borrie, Xue Teng	We propose a hybrid pathological analysis model, which integrates manifold learning and Random Vector functional-link network (RVFL) so as to achieve better ability to extract discriminant information with limited training materials.
633	Mixed Discrete-Continuous Planning with Convex Optimization	Enrique Fernandez-Gonzalez, Erez Karpas, Brian Williams	We introduce cqScotty, a heuristic forward search planner that solves these problems efficiently.
634	Integration of Planning with Recognition for Responsive Interaction Using Classical Planners	Richard G. Freedman, Shlomo Zilberstein	We propose an integration of planning with probabilistic recognition where each method uses intermediate results from the other as a guiding heuristic for recognition of the plan/goal in-progress as well as the interactive response.
635	Configuration Planning with Temporal Constraints	Uwe Köckemann, Lars Karlsson	We propose and compare two approaches to configuration planning.
636	Learning to Predict Intent from Gaze During Robotic Hand-Eye Coordination	Yosef Razin, Karen Feigh	This research compares the application of various machine learning methods to intent prediction from gaze tracking data during robotic hand-eye coordination tasks.
637	Vision-Language Fusion for Object Recognition	Sz-Rung Shiang, Stephanie Rosenthal, Anatole Gershman, Jaime Carbonell, Jean Oh	In this paper, we develop an algorithm to improve object recognition by integrating human-generated contextual information with vision algorithms.
638	State Projection via AI Planning	Shirin Sohrabi, Anton V. Riabov, Octavian Udrea	In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-as-planning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review.
639	Building Task-Oriented Dialogue Systems for Online Shopping	Zhao Yan, Nan Duan, Peng Chen, Ming Zhou, Jianshe Zhou, Zhoujun Li	We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner.