Paper Digest: IJCAI 2017 Highlights

August 18, 2017October 9, 2019 admin

International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. In 2017, it is to be held in Melbourne, Australia. There were more than 2,540 paper submissions, of which 660 were accepted.

To help AI community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / 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
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TABLE 1: IJCAI 2017 Papers

	Title	Authors	Highlight
1	Proactive and Reactive Coordination of Non-dedicated Agent Teams Operating in Uncertain Environments	Pritee Agrawal, Pradeep Varakantham	To that end, we first provide a general model to represent non-dedicated teams. Second, we provide a proactive approach based on sample average approximation to generate a strategy that works well across different feasible scenarios of agents leaving the team.
2	Diverse Weighted Bipartite b-Matching	Faez Ahmed, John P. Dickerson, Mark Fuge	Adapting a classical definition of the diversity of a set, we propose a quadratic programming-based approach to solving a submodular minimization problem that balances diversity and total weight of the solution.
3	Pure Nash Equilibria in Online Fair Division	Martin Aleksandrov, Toby Walsh	We consider a fair division setting in which items arrive one by one and are allocated to agents via two existing mechanisms: LIKE and BALANCED LIKE.
4	Efficient Computation of Extensions for Dynamic Abstract Argumentation Frameworks: An Incremental Approach	Gianvincenzo Alfano, Sergio Greco, Francesco Parisi	In this paper, we tackle the problem of incrementally computing extensions for dynamic AFs: given an initial extension and an update (or a set of updates), we devise a technique for computing an extension of the updated AF under four well-known semantics (i.e., complete, preferred, stable, and grounded).
5	Acceptability Semantics for Weighted Argumentation Frameworks	Leila Amgoud, Jonathan Ben-Naim, Dragan Doder, Srdjan Vesic	Acceptability Semantics for Weighted Argumentation Frameworks
6	Measuring the Intensity of Attacks in Argumentation Graphs with Shapley Value	Leila Amgoud, Jonathan Ben-Naim, Srdjan Vesic	This paper introduces the novel concept of contribution measure which evaluates those contributions.
7	An Abstraction-Refinement Methodology for Reasoning about Network Games	Guy Avni, Shibashis Guha, Orna Kupferman	We describe an abstraction-refinement methodology for reasoning about NGs.
8	Pareto Optimal Allocation under Uncertain Preferences	Haris Aziz, Ronald de Haan, Baharak Rastegari	For both of these models, we present a number of algorithmic and complexity results highlighting the difference and similarities in the complexity of the two models.
9	The Condorcet Principle for Multiwinner Elections: From Shortlisting to Proportionality	Haris Aziz, Edith Elkind, Piotr Faliszewski, Martin Lackner, Piotr Skowron	The goal of this paper is to explore these two notions, their implications on restricted domains, and the computational complexity of rules that are consistent with them.
10	Verification of Broadcasting Multi-Agent Systems against an Epistemic Strategy Logic	Francesco Belardinelli, Alessio Lomuscio, Aniello Murano, Sasha Rubin	We study a class of synchronous, perfect-recall multi-agent systemswith imperfect information and broadcasting (i.e., fully observableactions).
11	Parameterised Verification of Data-aware Multi-Agent Systems	Francesco Belardinelli, Panagiotis Kouvaros, Alessio Lomuscio	We introduce parameterised data-aware multi-agent systems, a formalism to reason about the temporal-epistemic properties of arbitrarily large collections of homogeneous agents, each operating on an infinite data domain.
12	Equilibria in Ordinal Games: A Framework based on Possibility Theory.	Nahla Ben Amor, Helene Fargier, Régis Sabbadin	The present paper proposes the first definition of mixed equilibrium for ordinal games.
13	Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos	Aniket Bera, Tanmay Randhavane, Dinesh Manocha	We present a real-time algorithm to automatically classify the behavior or personality of a pedestrian based on his or her movements in a crowd video.
14	Computing Bayes-Nash Equilibria in Combinatorial Auctions with Continuous Value and Action Spaces	Vitor Bosshard, Benedikt Bünz, Benjamin Lubin, Sven Seuken	In this paper, we present a fast, general algorithm for computing symmetric pure ε-BNEs in CAs with continuous values and actions. Finally, we introduce the new Multi-Minded LLLLGG domain with eight goods and six bidders, and apply our algorithm to finding an equilibrium in this domain.
15	Voting by sequential elimination with few voters	Sylvain Bouveret, Yann Chevaleyre, François Durand, Jérôme Lang	We define a new class of low-communication voting rules, tailored for contexts with few voters and possibly many candidates.
16	Fair Division of a Graph	Sylvain Bouveret, Katarína Cechlárová, Edith Elkind, Ayumi Igarashi, Dominik Peters	We consider fair allocation of indivisible items under an additional constraint: there is an undirected graph describing the relationship between the items, and each agent’s share must form a connected subgraph of this graph.
17	Bounding the Inefficiency of Compromise	Ioannis Caragiannis, Panagiotis Kanellopoulos, Alexandros A. Voudouris	They have facilitated information dissemination in ways that have been beneficial for their users but it is also a common belief that they are often used strategically in order to spread information that only serves the objectives of particular users.
18	Learning a Ground Truth Ranking Using Noisy Approval Votes	Ioannis Caragiannis, Evi Micha	We consider a voting scenario where agents have opinions that are estimates of an underlying common ground truth ranking of the available alternatives, and each agent is asked to approve a set with her most preferred alternatives.
19	Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy	Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, Subbarao Kambhampati	In particular, we show how explanation can be seen as a “model reconciliation problem” (MRP), where the AI system in effect suggests changes to the human’s model, so as to make its plan be optimal with respect to that changed human model.
20	Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits	Mithun Chakraborty, Kai Yee Phoebe Chua, Sanmay Das, Brendan Juba	In this paper, we introduce a multi-agent multi-armed bandit-based model for ad hoc teamwork with expensive communication.
21	Pessimistic Leader-Follower Equilibria with Multiple Followers	Stefano Coniglio, Nicola Gatti, Alberto Marchesi	In this paper, we address the multi-follower case for normal-form games, assuming that, after observing the leader’s commitment, the followers play pure strategies and reach a Nash equilibrium.
22	No Pizza for You: Value-based Plan Selection in BDI Agents	Stephen Cranefield, Michael Winikoff, Virginia Dignum, Frank Dignum	This paper presents a model of agent behavior that takes into account user preferences and moral values.
23	Interaction-based ontology alignment repair with expansion and relaxation	Jérôme Euzenat	We introduce new adaptation operators that improve those previously considered.
24	Multiwinner Rules on Paths From k-Borda to Chamberlin–Courant	Piotr Faliszewski, Piotr Skowron, Arkadii Slinko, Nimrod Talmon	We explore continuous transitions from k-Borda to Chamberlin-Courant and study intermediate rules.
25	Operation Frames and Clubs in Kidney Exchange	Gabriele Farina, John P. Dickerson, Tuomas Sandholm	We propose significant generalizations to kidney exchange.
26	A Novel Symbolic Approach to Verifying Epistemic Properties of Programs	Nikos Gorogiannis, Franco Raimondi, Ioana Boureanu	We introduce a framework for the symbolic verification of epistemic properties of programs expressed in a class of general-purpose programming languages.
27	Object Allocation via Swaps along a Social Network	Laurent Gourvès, Julien Lesca, Anaëlle Wilczynski	By considering that the agents are embedded in a social network, we propose to study the allocations emerging from a sequence of simple swaps between pairs of neighbors in the network.
28	The Off-Switch Game	Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, Stuart Russell	Our goal is to study the incentives an agent has to allow itself to be switched off.
29	Optimal Posted-Price Mechanism in Microtask Crowdsourcing	Zehong Hu, Jie Zhang	In this paper, we propose a novel posted-price mechanism which not only outperforms existing mechanisms on performance but also avoids their need of a finite price range.
30	Online Roommate Allocation Problem	Guangda Huzhang, Xin Huang, Shengyu Zhang, Xiaohui Bei	We study the online allocation problem under a roommate market model introduced in [Chan et al., 2016].
31	Near-Feasible Stable Matchings with Budget Constraints	Yasushi Kawase, Atsushi Iwasaki	This paper deals with two-sided matching with budget constraints where one side (firm or hospital) can make monetary transfers (offer wages) to the other (worker or doctor).
32	A Bayesian Approach to Argument-Based Reasoning for Attack Estimation	Hiroyuki Kido, Keishi Okamoto	This study investigates the utilisation of acceptability semantics to statistically estimate an attack relation between arguments wherein the acceptability statuses of arguments are provided.
33	Tosca: Operationalizing Commitments Over Information Protocols	Thomas C. King, Akın Günay, Amit K. Chopra, Munindar P. Singh	In this paper, we combine recent advances on specifying commitments and information protocols.
34	Omniscient Debugging for Cognitive Agent Programs	Vincent J. Koeman, Koen V. Hindriks, Catholijn M. Jonker	In this paper, we show that for agent-oriented programming practical omniscient debugging is possible.
35	Convergence and Quality of Iterative Voting Under Non-Scoring Rules	Aaron Koolyk, Tyrone Strangway, Omer Lev, Jeffrey S. Rosenschein	We consider iterative voting for non-scoring rules, examining the major ones, and show that none of them converge when assuming (as most research has so far) that voters pursue a best response strategy.
36	Constraint-Based Symmetry Detection in General Game Playing	Frédéric Koriche, Sylvain Lagrue, Éric Piette, Sébastien Tabary	In this paper, we develop an alternative approach to symmetry detection in stochastic games that exploits constraint programming techniques.
37	Verifying Fault-tolerance in Parameterised Multi-Agent Systems	Panagiotis Kouvaros, Alessio Lomuscio	We present a method for injecting a variety of non-ideal behaviours, or faults, studied in the safety-analysis literature into the abstract agent templates that are used to generate an unbounded family of multi-agent systems with different sizes.
38	Smoothing Method for Approximate Extensive-Form Perfect Equilibrium	Christian Kroer, Gabriele Farina, Tuomas Sandholm	In this paper, we provide, to our knowledge, the first extension of these methods to equilibrium refinements.
39	Contest Design with Uncertain Performance and Costly Participation	Priel Levy, David Sarne, Igor Rochlin	This paper studies the problem of designing contests for settings where a principal seeks to optimize the quality of the best performance obtained, and potential contestants only strategize about whether to participate in the contest, as participation incurs some cost.
40	Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media	Lizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Xu, Tat-Seng Chua	To help companies monitor their brands, in this work, we delve into the task of extracting representative aspects and posts from users’ free-text posts in social media.
41	Crowd Learning: Improving Online Decision Making Using Crowdsourced Data	Yang Liu, Mingyan Liu	We analyze an online learning problem that arises in crowdsourcing systems for users facing crowdsourced data: a user at each discrete time step t can choose K out of a total of N options (bandits), and receives randomly generated rewards dependent on user-specific and option-specific statistics unknown to the user.
42	Recognizing Top-Monotonic Preference Profiles in Polynomial Time	Krzysztof Magiera, Piotr Faliszewski	We provide the first polynomial-time algorithm for recognizing if a profile of (possibly weak) preference orders is top-monotonic.
43	Probability Bounds for Overlapping Coalition Formation	Michail Mamakos, Georgios Chalkiadakis	In this work, we provide novel methods which benefit from obtained probability bounds for assessing the ability of teams of agents to accomplish coalitional tasks.
44	Computing an Approximately Optimal Agreeable Set of Items	Pasin Manurangsi, Warut Suksompong	Our goal in this paper is to efficiently compute an agreeable subset whose size approximates the size of the smallest agreeable subset for a given instance.
45	Mechanisms for Online Organ Matching	Nicholas Mattei, Abdallah Saffidine, Toby Walsh	We propose a simple mechanisms to perform this matching and compare this new mechanism with the more complex algorithm currently under consideration by the Organ and Tissue Authority in Australia.
46	Deterministic, Strategyproof, and Fair Cake Cutting	Vijay Menon, Kate Larson	We study the classic cake cutting problem from a mechanism design perspective, in particular focusing on deterministic mechanisms that are strategyproof and fair.
47	Core Stability in Hedonic Games among Friends and Enemies: Impact of Neutrals	Kazunori Ohta, Nathanaël Barrot, Anisse Ismaili, Yuko Sakurai, Makoto Yokoo	We investigate hedonic games under enemies aversion and friends appreciation, where every agent considers other agents as either a friend or an enemy.
48	Multiple-Profile Prediction-of-Use Games	Andrew Perrault, Craig Boutilier	Despite this, MPOU games introduce new incentive issues that prevent the consequences of convexity from being exploited directly, a problem we analyze and resolve.
49	Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach	Sébastien Picault, Yu-Lin Huang, Vianney Sicard, Pauline Ezanno	We explain in this paper how this approach is used for building more generic, reliable and sustainable simulations, illustrated by real-case applications in cattle epidemiology.
50	Don’t Bury your Head in Warnings: A Game-Theoretic Approach for Intelligent Allocation of Cyber-security Alerts	Aaron Schlenker, Haifeng Xu, Mina Guirguis, Christopher Kiekintveld, Arunesh Sinha, Milind Tambe, Solomon Sonya, Darryl Balderas, Noah Dunstatter	We address this challenge with the four following contributions: (1) a cyber screening game (CSG) model for the cyber network protection domain, (2) an NP-hardness proof for computing the optimal strategy for the defender, (3) an algorithm that finds the optimal allocation of experts to alerts in the CSG, and (4) heuristic improvements for computing allocations in CSGs that accomplishes significant scale-up which we show empirically to closely match the solution quality of the optimal algorithm.
51	Posted Pricing sans Discrimination	Shreyas Sekar	Our central contribution is a structured framework for decision making and static item pricing in the face of uncertainty and production costs, i.e., the seller decides how much to produce and posts a single price per good that is common to all buyers, the buyers arrive sequentially and purchase utility maximizing bundles of goods.
52	Why You Should Charge Your Friends for Borrowing Your Stuff	Kijung Shin, Euiwoong Lee, Dhivya Eswaran, Ariel D. Procaccia	We consider goods that can be shared with k-hop neighbors (i.e., the set of nodes within k hops from an owner) on a social network.
53	Synchronisation Games on Hypergraphs	Sunil Simon, Dominik Wojtczak	We study a strategic game model on hypergraphs where players, modelled by nodes, try to coordinate or anti-coordinate their choices within certain groups of players, modelled by hyperedges.
54	Proportional Rankings	Piotr Skowron, Martin Lackner, Markus Brill, Dominik Peters, Edith Elkind	We extend the principle of proportional representation to rankings: given approval preferences, we aim to generate aggregate rankings so that cohesive groups of voters are represented proportionally in each initial segment of the ranking.
55	Attachment Centrality for Weighted Graphs	Jadwiga Sosnowska, Oskar Skibski	To analyse such settings, in this paper we extend the Attachment Centrality to node-weighted and edge-weighted graphs.
56	Online Optimization of Video-Ad Allocation	Hanna Sumita, Yasushi Kawase, Sumio Fujita, Takuro Fukunaga	In this paper, we study the video advertising in the context of internet advertising.
57	On the Power and Limitations of Deception in Multi-Robot Adversarial Patrolling	Noga Talmor, Noa Agmon	In this paper we present a novel defending approach which manipulates the adversarial (possibly partial) knowledge on the patrolling robots, so that it will believe the robots have more power than they actually have.
58	Manipulating Gale-Shapley Algorithm: Preserving Stability and Remaining Inconspicuous	Rohit Vaish, Dinesh Garg	We study the problem of manipulation of the men-proposing Gale-Shapley algorithm by a single woman via permutation of her true preference list.
59	Multi-Agent Planning with Baseline Regret Minimization	Feng Wu, Shlomo Zilberstein, Xiaoping Chen	We propose a novel baseline regret minimization algorithm for multi-agent planning problems modeled as finite-horizon decentralized POMDPs.
60	Score Aggregation via Spectral Method	Mingyu Xiao, Yuqing Wang	In this paper, we give good algebraic and geometric explanations for score aggregation, and develop a spectral method for it.
61	Agent Design Consistency Checking via Planning	Nitin Yadav, John Thangarajah, Sebastian Sardina	In this work we present a novel approach to check the consistency of agent designs (prior to any implementation) with respect to the requirements specifications via automated planning.
62	How to Form Winning Coalitions in Mixed Human-Computer Settings	Yair Zick, Kobi Gal, Yoram Bachrach, Moshe Mash	This paper proposes a new negotiation game, based on the weighted voting paradigm in cooperative games, where players need to form coalitions and agree on how to share the gains.
63	Estimating the size of search trees by sampling with domain knowledge	Gleb Belov, Samuel Esler, Dylan Fernando, Pierre Le Bodic, George L. Nemhauser	We show how recently-defined abstract models of the Branch-and-Bound algorithm can be used to obtain information on how the nodes are distributed in B&B search trees.
64	An Admissible HTN Planning Heuristic	Pascal Bercher, Gregor Behnke, Daniel Höller, Susanne Biundo	Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically.
65	Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions	Jingwei Chen, Robert C. Holte, Sandra Zilles, Nathan R. Sturtevant	This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions.
66	Compromise-free Pathfinding on a Navigation Mesh	Michael Cui, Daniel D. Harabor, Alban Grastien	In this work we present a new pathfinding algorithm which is compromise-free; i.e. it is simultaneously fast, online and optimal.
67	A Random Model for Argumentation Framework: Phase Transitions, Empirical Hardness, and Heuristics	Yong Gao	We propose and study, theoretically and empirically, a new random model for the abstract argumentation framework (AF).
68	Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads	Carlos Hernandez, Adi Botea, Jorge A. Baier, Vadim Bulitko	In this paper, we propose a novel pruning approach applicable to the wide class of real-time search algorithms.
69	A Reduction based Method for Coloring Very Large Graphs	Jinkun Lin, Shaowei Cai, Chuan Luo, Kaile Su	This paper explores techniques for solving GCP on very large real world graphs.We first propose a reduction rule for GCP, which is based on a novel concept called degree bounded independent set.The rule is iteratively executed by interleaving between lower bound computation and graph reduction.
70	The Mixing of Markov Chains on Linear Extensions in Practice	Topi Talvitie, Teppo Niinimäki, Mikko Koivisto	We investigate almost uniform sampling from the set of linear extensions of a given partial order.
71	Generating Hard Random Boolean Formulas and Disjunctive Logic Programs	Giovanni Amendola, Francesco Ricca, Miroslaw Truszczynski	We propose a model of random quantified boolean formulas and their natural random disjunctive logic program counterparts.
72	Stochastic Constraint Programming with And-Or Branch-and-Bound	Behrouz Babaki, Tias Guns, Luc de Raedt	We show how a state-of-the-art probabilistic inference engine can be integrated into standard constraint solvers.
73	Scalable Constraint-based Virtual Data Center Allocation	Sam Bayless, Nodir Kodirov, Ivan Beschastnikh, Holger H. Hoos, Alan J. Hu	To solve this problem, we introduce NETSOLVER, which is based on the general-purpose constraint solver MONOSAT.
74	Compact MDDs for Pseudo-Boolean Constraints with At-Most-One Relations in Resource-Constrained Scheduling Problems	Miquel Bofill, Jordi Coll, Josep Suy, Mateu Villaret	In this work we introduce a way to take advantage of these implicit relations to obtain a compact Multi-Decision Diagram (MDD) representation for those PB constraints.
75	Relaxed Exists-Step Plans in Planning as SMT	Miquel Bofill, Joan Espasa, Mateu Villaret	In this paper we introduce a new encoding for planning as SMT, which adheres to the relaxed relaxed ∃-step (R 2 ∃-step) semantics for parallel plans.
76	From Decimation to Local Search and Back: A New Approach to MaxSAT	Shaowei Cai, Chuan Luo, Haochen Zhang	This work proposes a new incomplete approach to MaxSAT.
77	On the Kernelization of Global Constraints	Clément Carbonnel, Emmanuel Hebrard	We showcase the theoretical interest of our ideas on two constraints, VertexCover and EdgeDominatingSet.
78	The DNA Word Design Problem: A New Constraint Model and New Results	Michael Codish, Michael Frank, Vitaly Lagoon	We describe a new constraint model for this problem and demonstrate that it improves on previous solutions (computes better lower bounds) for various instances of the problem.
79	Learning-Based Abstractions for Nonlinear Constraint Solving	Sumanth Dathathri, Nikos Arechiga, Sicun Gao, Richard M. Murray	We propose a new abstraction refinement procedure based on machine learning to improve the performance of nonlinear constraint solving algorithms on large-scale problems.
80	The Hard Problems Are Almost Everywhere For Random CNF-XOR Formulas	Jeffrey M. Dudek, Kuldeep S. Meel, Moshe Y. Vardi	In this paper, we present the first study of the runtime behavior of SAT solvers equipped with XOR-reasoning techniques on random CNF-XOR formulas.
81	Solving Integer Linear Programs with a Small Number of Global Variables and Constraints	Pavel Dvořák, Eduard Eiben, Robert Ganian, Dušan Knop, Sebastian Ordyniak	Our main contributions can be divided into three parts.
82	Personnel Scheduling as Satisfiability Modulo Theories	Christoph Erkinger, Nysret Musliu	We propose two new modeling techniques for RWS that encode the problem using formulas over different background theories.
83	Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation	Yi Fan, Nan Li, Chengqian Li, Zongjie Ma, Longin Jan Latecki, Kaile Su	In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC.
84	Finding Robust Solutions to Stable Marriage	Begum Genc, Mohamed Siala, Barry O’Sullivan, Gilles Simonin	We study the notion of robustness in stable matching problems.
85	Locality in Random SAT Instances	Jesús Giráldez-Cru, Jordi Levy	In this paper, we present a random SAT instances generator based on the notion of locality.
86	A Core-Guided Approach to Learning Optimal Causal Graphs	Antti Hyttinen, Paul Saikko, Matti Järvisalo	We propose several domain-specific techniques and integrate them into a core-guided maximum satisfiability solver, thereby speeding up current state of the art in exact search for causal graphs with cycles and latent confounders on simulated and real-world data.
87	Cardinality Encodings for Graph Optimization Problems	Alexey Ignatiev, Antonio Morgado, Joao Marques-Silva	Different optimization problems defined on graphs find application in complex network analysis.
88	Learning to Run Heuristics in Tree Search	Elias B. Khalil, Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed, Yufen Shao	In this work, we study the problem of deciding at which node a heuristic should be run, such that the overall (primal) performance of the solver is optimized.
89	An Improved Decision-DNNF Compiler	Jean-Marie Lagniez, Pierre Marquis	We present and evaluate a new compiler, called d4, targeting the Decision-DNNF language.
90	A Recursive Shortcut for CEGAR: Application To The Modal Logic K Satisfiability Problem	Jean-Marie Lagniez, Daniel Le Berre, Tiago de Lima, Valentin Montmirail	In this paper, we propose a new CEGAR-like approach for tackling PSPACE complete problems that we call RECAR (Recursive Explore and Check Abstraction Refinement).
91	Automatic Synthesis of Smart Table Constraints by Abstraction of Table Constraints	Baudouin Le Charlier, Minh Thanh Khong, Christophe Lecoutre, Yves Deville	In this paper, we propose an algorithm to convert automatically any (ordinary) table into a compact smart table.
92	Solving Stochastic Boolean Satisfiability under Random-Exist Quantification	Nian-Ze Lee, Yen-Shi Wang, Jie-Hong R. Jiang	This paper focuses on random-exist quantified SSAT formulas, and proposes an algorithm combining binary decision diagram (BDD), logic synthesis, and modern SAT techniques to improve computational efficiency.
93	Enhancing Campaign Design in Crowdfunding: A Product Supply Optimization Perspective	Qi Liu, Guifeng Wang, Hongke Zhao, Chuanren Liu, Tong Xu, Enhong Chen	Specifically, given the expected budget and the perks of a campaign, we propose a novel solution to automatically recommend the optimal product supply to every perk for balancing the expected return of this campaign against the risk.
94	An Effective Learnt Clause Minimization Approach for CDCL SAT Solvers	Mao Luo, Chu-Min Li, Fan Xiao, Felip Manyà, Zhipeng Lü	To overcome this drawback, we define a new inprocessing SAT approach which eliminates redundant literals from learnt clauses by applying Boolean constraint propagation.
95	A Partitioning Algorithm for Maximum Common Subgraph Problems	Ciaran McCreesh, Patrick Prosser, James Trimble	We introduce a new branch and bound algorithm for the maximum common subgraph and maximum common connected subgraph problems which is based around vertex labelling and partitioning.
96	Efficient Weighted Model Integration via SMT-Based Predicate Abstraction	Paolo Morettin, Andrea Passerini, Roberto Sebastiani	In this paper we present a novel general notion of WMI, which fixes the theoretical limitations and allows for exploiting the power of SMT-based predicate abstraction techniques.
97	Constraint Games revisited	Anthony Palmieri, Arnaud Lallouet	In this paper, we rethink their solving technique in terms of constraint propagation by considering players preferences as global constraints.
98	On Neighborhood Singleton Consistencies	Anastasia Paparrizou, Kostas Stergiou	Using the recently proposed variant of SAC called Neighborhood SAC as basis, we propose a family of weaker singleton consistencies.
99	Temporal Planning with Clock-Based SMT Encodings	Jussi Rintanen	We propose more scalable encodings of temporal planning in SMT.
100	Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming	Buser Say, Ga Wu, Yu Qing Zhou, Scott Sanner	In this paper, we make the critical observation that the popular Rectified Linear Unit (ReLU) transfer function for deep networks not only allows accurate nonlinear deep net model learning, but also permits a direct compilation of the deep network transition model to a Mixed-Integer Linear Program (MILP) encoding in a planner we call Hybrid Deep MILP Planning (HD-MILP-PLAN).
101	Player Movement Models for Video Game Level Generation	Sam Snodgrass, Santiago Ontañón	We are interested in extracting player models automatically from play traces and using those learned models, paired with a machine learning-based generator to create levels that allow the same types of movements observed in the play traces.
102	On the Computational Complexity of Gossip Protocols	Krzysztof R. Apt, Eryk Kopczyński, Dominik Wojtczak	We show that for any monotonic type of calls the implementability of a distributed epistemic gossip protocol is a P^{NP}_{\|\|}-complete problem, while the problems of its partial correctness and termination are in coNP^{NP}.
103	Epistemic-entrenchment Characterization of Parikh’s Axiom	Theofanis Aravanis, Pavlos Peppas, Mary-Anne Williams	In this article, we provide the epistemic-entrenchment characterization of the weak version of Parikh’s relevance-sensitive axiom for belief revision — known as axiom (P) — for the general case of incomplete theories.
104	Weakening Covert Networks by Minimizing Inverse Geodesic Length	Haris Aziz, Serge Gaspers, Kamran Najeebullah	In this paper, we undertake a study of the classical and parameterized complexity of the MinIGL problem.
105	Query Rewriting for DL-Lite with n-ary Concrete Domains	Franz Baader, Stefan Borgwardt, Marcel Lippmann	We introduce restrictions on these predicates and on the ontology language that allow us to reduce OBQA to query answering in databases using the so-called combined rewriting approach.
106	Answering Conjunctive Regular Path Queries over Guarded Existential Rules	Jean-François Baget, Meghyn Bienvenu, Marie-Laure Mugnier, Michael Thomazo	In this paper, we investigate the complexity of answering two-way conjunctive regular path queries (CRPQs) over knowledge bases whose ontology is given by a set of guarded existential rules.
107	A General Notion of Equivalence for Abstract Argumentation	Ringo Baumann, Wolfgang Dvořák, Thomas Linsbichler, Stefan Woltran	We introduce a parametrized equivalence notion for abstract argumentation that subsumes standard and strong equivalence as corner cases.
108	A Study of Unrestricted Abstract Argumentation Frameworks	Ringo Baumann, Christof Spanring	Inthis paper we study a bunch of abstract propertieslike SCC-recursiveness, expressiveness or intertrans-latability for unrestricted AFs.
109	A Model for Accountable Ordinal Sorting	Khaled Belahcene, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, Wassila Ouerdane	We address the problem of multicriteria ordinalsorting through the lens of accountability, i.e. theability of a human decision-maker to own a recommendationmade by the system.
110	Dynamic Logic for Data-aware Systems: Decidability Results	Francesco Belardinelli, Andreas Herzig	We introduce a first-order extension of dynamic logic (FO-DL), suitable to represent and reason about the behaviour of Data-aware Systems (DaS), which are systems whose data content is explicitly exhibited in the system’s description.
111	Reasoning about Probabilities in Unbounded First-Order Dynamical Domains	Vaishak Belle, Gerhard Lakemeyer	In this paper, we reconsider that model of belief, and propose a new logical variant that has much of the expressive power of the original, but goes beyond it in novel ways.
112	Reformulating Queries: Theory and Practice	Michael Benedikt, Egor V. Kostylev, Fabio Mogavero, Efthymia Tsamoura	We consider a setting where a user wants to pose a query against a dataset where background knowledge, expressed as logical sentences, is available, but only a subset of the information can be used to answer the query.
113	Ontology-Mediated Query Answering for Key-Value Stores	Meghyn Bienvenu, Pierre Bourhis, Marie-Laure Mugnier, Sophie Tison, Federico Ulliana	We propose a novel rule-based ontology language for JSON records and investigate its computational properties.
114	The Impact of Treewidth on ASP Grounding and Solving	Bernhard Bliem, Marius Moldovan, Michael Morak, Stefan Woltran	In this paper, we aim to study how the performance of modern answer set programming (ASP) solvers is influenced by the treewidth of the input program and to investigate the consequences of this relationship.
115	Safe Inductions: An Algebraic Study	Bart Bogaerts, Joost Vennekens, Marc Denecker	In this paper, we formally define the safety criterion algebraically.
116	Semantics for Active Integrity Constraints Using Approximation Fixpoint Theory	Bart Bogaerts, Luís Cruz-Filipe	In this paper, we apply approximation fixpoint theory, an algebraic framework that unifies semantics of non-monotonic logics, to the field of AICs.
117	Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints	Blai Bonet, Giuseppe De Giacomo, Hector Geffner, Sasha Rubin	In this work, we shed light on why this termination condition is needed and how it can be removed.
118	Making Cross Products and Guarded Ontology Languages Compatible	Pierre Bourhis, Michael Morak, Andreas Pieris	Our goal is to give an answer to the above question.
119	On Coalitional Manipulation for Multiwinner Elections: Shortlisting	Robert Bredereck, Andrzej Kaczmarczyk, Rolf Niedermeier	In particular, we investigate the influence of several tie-breaking mechanisms (e.g. pessimistic versus optimistic) and group evaluation functions (e.g. egalitarian versus utilitarian) and conclude that in an egalitarian setting strategic voting may indeed be computationally intractable regardless of the tie-breaking rule.
120	Manipulating Opinion Diffusion in Social Networks	Robert Bredereck, Edith Elkind	We consider several ways of manipulating the majority opinion in a stable outcome, such as bribing agents, adding/deleting links, and changing the order of updates, and investigate the computational complexity of the associated problems, identifying tractable and intractable cases.
121	Strong Inconsistency in Nonmonotonic Reasoning	Gerhard Brewka, Matthias Thimm, Markus Ulbricht	We investigate the complexity of various related reasoning problems and present a generic algorithm for computing minimal strongly inconsistent subsets of a knowledge base.
122	Classical Generalized Probabilistic Satisfiability	Carlos Caleiro, Filipe Casal, Andreia Mordido	Here, we present a polynomial reduction of GGenPSAT to SMT over the quantifier-free theory of linear integer and real arithmetic.
123	Budget-Constrained Dynamics in Multiagent Systems	Rui Cao, Pavel Naumov	The paper introduces a notion of a budget-constrained multiagent transition system that associates two financial parameters with each transition: a pre-transition minimal budget requirement and a post-transition profit.
124	Restricted Chase (Non)Termination for Existential Rules with Disjunctions	David Carral, Irina Dragoste, Markus Krötzsch	We develop acyclicity conditions to ensure its termination.
125	Belief Change in a Preferential Non-monotonic Framework	Giovanni Casini, Thomas Meyer	In this paper we show that it also makes sense to analyse belief change within a (preferential) non-monotonic framework.
126	An Algorithm for Constructing and Solving Imperfect Recall Abstractions of Large Extensive-Form Games	Jiri Cermak, Branislav Bošanský, Viliam Lisý	We propose a new algorithm based on fictitious play that significantly reduces memory requirements for storing average strategies.
127	Query Answering in Ontologies under Preference Rankings	İsmail İlkan Ceylan, Thomas Lukasiewicz, Rafael Peñaloza, Oana Tifrea-Marciuska	We present an ontological framework, based on preference rankings, that allows users to express their preferences between the knowledge explicitly available in the ontology.
128	Most Probable Explanations for Probabilistic Database Queries	İsmail İlkan Ceylan, Stefan Borgwardt, Thomas Lukasiewicz	We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.
129	Learning from Ontology Streams with Semantic Concept Drift	Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan, Huajun Chen	To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings.
130	Discriminative Dictionary Learning With Ranking Metric Embedded for Person Re-Identification	De Cheng, Xiaojun Chang, Li Liu, Alexander G. Hauptmann, Yihong Gong, Nanning Zheng	In this paper, we propose a novel dictionary learning based method with the ranking metric embedded, for person Re-Id.
131	Handling non-local dead-ends in Agent Planning Programs	Lukas Chrpa, Nir Lipovetzky, Sebastian Sardina	We propose an approach to reason about agent planning programs with global information.
132	Inferring Implicit Event Locations from Context with Distributional Similarities	Jin-Woo Chung, Wonsuk Yang, Jinseon You, Jong C. Park	To overcome this limitation for the first time, we present a system that infers the implicit event locations from a given document.
133	Temporal Sequences of Qualitative Information: Reasoning about the Topology of Constant-Size Moving Regions	Quentin Cohen-Solal, Maroua Bouzid, Alexandre Niveau	Relying on the recently introduced multi-algebras, we present a general approach for reasoning about temporal sequences of qualitative information that is generally more efficient than existing techniques.
134	On Querying Incomplete Information in Databases under Bag Semantics	Marco Console, Paolo Guagliardo, Leonid Libkin	We show that the behavior of positive queries is different under bag semantics: finding the minimum number of occurrences can still be done efficiently, but for maximum it becomes intractable.
135	Streaming Multi-Context Systems	Minh Dao-Tran, Thomas Eiter	We thus present streaming MCS, which have a run-based semantics that accounts for asynchronous, distributed execution and supports obtaining equilibria for contexts in cyclic exchange (avoiding infinite loops); moreover, they equip MCS with native stream reasoning features.
136	Bounded Timed Propositional Temporal Logic with Past Captures Timeline-based Planning with Bounded Constraints	Dario Della Monica, Nicola Gigante, Angelo Montanari, Pietro Sala, Guido Sciavicco	This paper shows that timeline-based planning with bounded temporal constraints can be captured by a bounded version of Timed Propositional Temporal Logic, augmented with past operators, which is an extension of LTL originally designed for the verification of real-time systems.
137	Lazy-Grounding for Answer Set Programs with External Source Access	Thomas Eiter, Tobias Kaminski, Antonius Weinzierl	We explore this issue and present a new evaluation algorithm for HEX-programs based on lazy-grounding solving for ASP.
138	Process Plan Controllers for Non-Deterministic Manufacturing Systems	Paolo Felli, Lavindra de Silva, Brian Logan, Svetan Ratchev	In this paper we present an approach to the automated computation of process plans and process plan controllers.
139	Strategically knowing how	Raul Fervari, Andreas Herzig, Yanjun Li, Yanjing Wang	In this paper, we propose a single-agent logic of goal-directed knowing how extending the standard epistemic logic of knowing that with a new knowing how operator.
140	What Can You Do with a Rock? Affordance Extraction via Word Embeddings	Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate	This paper presents a method for affordance extraction via word embeddings trained on a tagged Wikipedia corpus.
141	The Tractability of the Shapley Value over Bounded Treewidth Matching Games	Gianluigi Greco, Francesco Lupia, Francesco Scarcello	The main contribution of the paper is to provide a positive answer to this question, by showing that the Shapley value is tractable for matching games defined over graphs having bounded treewidth.
142	Non-Determinism and the Dynamics of Knowledge	Davide Grossi, Andreas Herzig, Wiebe van der Hoek, Christos Moyzes	In this paper we attempt to shed light on the concept of an agent’s knowledge after a non-deterministic action is executed.
143	Relatedness-based Multi-Entity Summarization	Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng	In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation.
144	Nash Equilibria in Concurrent Games with Lexicographic Preferences	Julian Gutierrez, Aniello Murano, Giuseppe Perelli, Sasha Rubin, Michael Wooldridge	We study concurrent games with finite-memory strategies where players are given a Buchi and a mean-payoff objective, which are related by a lexicographic order: a player first prefers to satisfy its Buchi objective, and then prefers to minimise costs, which are given by a mean-payoff function.
145	Combining DL-Lite_{bool}^N with Branching Time: A gentle Marriage	Víctor Gutiérrez-Basulto, Jean Christoph Jung	For the resulting logics, we present algorithms for the satisfiability problem and (mostly tight) complexity bounds ranging from ExpTime to 3ExpTime.
146	Characterising the Manipulability of Boolean Games	Paul Harrenstein, Paolo Turrini, Michael Wooldridge	In this paper we close this problem, giving a complete characterisation of those mechanisms that can induce a set of outcomes of the game to be exactly the set of Nash Equilibrium outcomes.
147	Revisiting Unrestricted Rebut and Preferences in Structured Argumentation.	Jesse Heyninck, Christian Straßer	In this paper we show that ASPIC-, a system allowing for unrestricted rebuts, suffers from contamination problems.
148	A General Multi-agent Epistemic Planner Based on Higher-order Belief Change	Xiao Huang, Biqing Fang, Hai Wan, Yongmei Liu	In this paper, we propose a general representation language for multi-agent epistemic planning where the initial KB and the goal, the preconditions and effects of actions can be arbitrary multi-agent epistemic formulas, and the solution is an action tree branching on sensing results.To support efficient reasoning in the multi-agent KD45 logic, we make use of a normal form called alternative cover disjunctive formula (ACDF).
149	ATL Strategic Reasoning Meets Correlated Equilibrium	Xiaowei Huang, Ji Ruan	We implement the extension into a software model checker and use the tool to analyse the examples in the paper.
150	Belief Manipulation Through Propositional Announcements	Aaron Hunter, François Schwarzentruber, Eric Tsang	In this paper, we consider not necessarily truthful public announcements in the setting of AGM belief revision.
151	Query Conservative Extensions in Horn Description Logics with Inverse Roles	Jean Christoph Jung, Carsten Lutz, Mauricio Martel, Thomas Schneider	We investigate the decidability and computational complexity of query conservative extensions in Horn description logics (DLs) with inverse roles.
152	Foundations of Declarative Data Analysis Using Limit Datalog Programs	Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks	This language is known to be undecidable, so we propose two fragments.
153	Strong Syntax Splitting for Iterated Belief Revision	Gabriele Kern-Isberner, Gerhard Brewka	In this paper we generalize syntax splitting from logical sentences to epistemic states, a step which is necessary to cover iterated revision.
154	Model Checking Multi-Agent Systems against LDLK Specifications	Jeremy Kong, Alessio Lomuscio	We introduce MCMAS LDLK , an extension of the open-source model checker MCMAS, implementing the algorithm and discuss the experimental results obtained.
155	On the Complexity of Enumerating the Extensions of Abstract Argumentation Frameworks	Markus Kröll, Reinhard Pichler, Stefan Woltran	The goal of this paper is to fill this gap.
156	Induction of Interpretable Possibilistic Logic Theories from Relational Data	Ondrej Kuzelka, Jesse Davis, Steven Schockaert	To address this, we propose a new SRL method which uses possibilistic logic to encode relational models.
157	Mapping Repair in Ontology-based Data Access Evolving Systems	Domenico Lembo, Riccardo Rosati, Valerio Santarelli, Domenico Fabio Savo, Evgenij Thorstensen	In this paper we study the evolution of ontology-based data access (OBDA) specifications, and focus on the case in which the ontology and/or the data source schema change, which may require a modification to the mapping between them to preserve both consistency and knowledge. We then present a set of results on the complexity of query answering in the above framework, when the ontology is expressed in DL-LiteR.
158	Temporalising Separation Logic for Planning with Search Control Knowledge	Xu Lu, Cong Tian, Zhenhua Duan	To this end, we propose a two-dimensional (spatial and temporal) logic namely PPTL^SL by temporalising separation logic with Propositional Projection Temporal Logic (PPTL).
159	A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations	Kangqi Luo, Xusheng Luo, Xianyang Chen, Kenny Q. Zhu	This paper studies the problem of discovering the structured knowledge representation of binary natural language relations.The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics.We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation.
160	Ontology-Mediated Querying with the Description Logic EL: Trichotomy and Linear Datalog Rewritability	Carsten Lutz, Leif Sabellek	We consider ontology-mediated queries (OMQs) based on an EL ontology and an atomic query (AQ), provide an ultimately fine-grained analysis of data complexity and study rewritability into linear Datalog-aiming to capture linear recursion in SQL.
161	Logic on MARS: Ontologies for Generalised Property Graphs	Maximilian Marx, Markus Krötzsch, Veronika Thost	We give a formalisation of a generalised notion of Property Graphs, called multi-attributed relational structures (MARS), and introduce a matching knowledge representation formalism, multi-attributed predicate logic (MAPL).
162	Context-aware Path Ranking for Knowledge Base Completion	Sahisnu Mazumder, Bing Liu	This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy.
163	Contract Design for Energy Demand Response	Reshef Meir, Hongyao Ma, Valentin Robu	We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing.
164	Dominance and Optimisation Based on Scale-Invariant Maximum Margin Preference Learning	Mojtaba Montazery, Nic Wilson	In this paper we define and analyse more cautious preference relations that are invariant to the scaling of features, or inputs, or both simultaneously; this leads to computational methods for testing dominance with respect to the induced relations, and for generating optimal solutions among a set of alternatives.
165	Generalized Target Assignment and Path Finding Using Answer Set Programming	Van Nguyen, Philipp Obermeier, Tran Cao Son, Torsten Schaub, William Yeoh	We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order.
166	The Bag Semantics of Ontology-Based Data Access	Charalampos Nikolaou, Egor V. Kostylev, George Konstantinidis, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks	Motivated by the need for OBDA systems supporting database-style aggregate queries, we propose a bag semantics for OBDA, where duplicate tuples in the views defined by the mappings are retained, as is the case in standard databases.
167	Efficient and Complete FD-solving for extended array constraints	Quentin Plazar, Mathieu Acher, Sébastien Bardin, Arnaud Gotlieb	This paper proposes an efficient and complete FD-solving technique for extended constraints over (possibly unbounded) arrays.
168	Conflict-driven ASP Solving with External Sources and Program Splits	Christoph Redl	In this work we introduce a new technique for conflict-driven learning over multiple program components.
169	A Reasoning System for a First-Order Logic of Limited Belief	Christoph Schwering	In this paper, we aim to go beyond the theory.
170	Fair Allocation based on Diminishing Differences	Erel Segal-Halevi, Haris Aziz, Avinatan Hassidim	Based on this characterization, we present a polynomial-time algorithm for finding a necessarily-DD-proportional allocation if it exists.
171	Efficiently Enforcing Path Consistency on Qualitative Constraint Networks by Use of Abstraction	Michael Sioutis, Jean-François Condotta	To this end, we propose a new algorithm, called PWCα, for efficiently enforcing partial weak path-consistency on qualitative constraint networks, that exploits the notion of abstraction for qualitative constraint networks, utilizes certain properties of partial weak path-consistency,and adapts the functionalities of some state-of-the-art algorithms to its design.
172	On Computing World Views of Epistemic Logic Programs	Tran Cao Son, Tiep Le, Patrick Kahl, Anthony Leclerc	This paper presents a novel algorithm for computing world views of different semantics of epistemic logic programs (ELP) and two of its realization, called Ep-asp (for an older semantics) and Ep-asp^{se} (for the newest semantics), whose implementation builds on the theoretical advancement in the study of ELPs and takes advantage of the multi-shot computation paradigm of the answer set solver Clingo.
173	GDL-III: A Description Language for Epistemic General Game Playing	Michael Thielscher	We develop a formal semantics for GDL-III and demonstrate that this language, despite its syntactic simplicity, is expressive enough to model the famous Muddy Children domain.
174	How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval	Rodrigo Toro Icarte, Jorge A. Baier, Cristian Ruz, Alvaro Soto	In this paper, we study how general-purpose ontologies—specifically, MIT’s ConceptNet ontology—can improve the performance of state-of-the-art vision systems.
175	Explicit Knowledge-based Reasoning for Visual Question Answering	Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, Anton van den Hengel	We describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a large-scale knowledge base. We also provide a dataset and a protocol by which to evaluate general visual question answering methods.
176	Inferring Human Attention by Learning Latent Intentions	Ping Wei, Dan Xie, Nanning Zheng, Song-Chun Zhu	We propose a probabilistic method to jointly model attention, intentions, and their interactions.
177	A Characterization Theorem for a Modal Description Logic	Paul Wild, Lutz Schröder	We prove a modal characterization theorem for this embedding, in analogy to results by van Benthem and Rosen relating ALC to standard first-order logic: We show that S5-ALC with only local roles is, both over finite and over unrestricted models, precisely the bisimulation-invariant fragment of S5-FOL, thus giving an exact description of the expressive power of S5-ALC with only local roles.
178	Efficient Inference and Computation of Optimal Alternatives for Preference Languages Based On Lexicographic Models	Nic Wilson, Anne-Marie George	Based on our framework, we show that testing consistency, and thus inference, is polynomial for a specific preference language which allows strict and non-strict statements, comparisons between outcomes and between partial tuples, both ceteris paribus and strong statements, and their combination.
179	Knowledge Graph Representation with Jointly Structural and Textual Encoding	Jiacheng Xu, Xipeng Qiu, Kan Chen, Xuanjing Huang	In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities.
180	Aggregating Crowd Wisdoms with Label-aware Autoencoders	Li’ang Yin, Jianhua Han, Weinan Zhang, Yong Yu	In this paper, we propose a novel framework named Label-Aware Autoencoders (LAA) to aggregate crowd wisdoms.
181	Proposing a Highly Accurate Hybrid Component-Based Factorised Preference Model in Recommender Systems	Farhad Zafari, Rasoul Rahmani, Irene Moser	In this paper, we introduce a new hybrid latent factor model that achieves great accuracy by integrating all these preference components in a unified model efficiently.
182	Transfer Learning in Multi-Armed Bandits: A Causal Approach	Junzhe Zhang, Elias Bareinboim	In this paper, we use causal inference as a basis to support a principled and more robust transfer of knowledge in RL settings.
183	Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination	Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf	In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions.
184	Role Forgetting for ALCOQH(universal role)-Ontologies Using an Ackermann-Based Approach	Yizheng Zhao, Renate A. Schmidt	In particular, we present a practical method for semantic role forgetting for ontologies expressible in the description logic ALCOQH(universal role), i.e., the basic description logic ALC extended with nominals, qualified number restrictions, role inclusions and the universal role.
185	Symbolic LTLf Synthesis	Shufang Zhu, Lucas M. Tabajara, Jianwen Li, Geguang Pu, Moshe Y. Vardi	In this paper, we propose a symbolic framework for LTLf synthesis based on this technique, by performing the computation over a representation of the DFA as a boolean formula rather than as an explicit graph.
186	A Unifying Framework for Probabilistic Belief Revision	Zhiqiang Zhuang, James Delgrande, Abhaya Nayak, Abdul Sattar	In this paper we provide a general, unifying framework for probabilistic belief revision.
187	Contextual Covariance Matrix Adaptation Evolutionary Strategies	Abbas Abdolmaleki, Bob Price, Nuno Lau, Luis Paulo Reis, Gerhard Neumann	In this paper, we extend the well known CMA-ES algorithm to the contextual setting and illustrate its performance on several contextual tasks.
188	RHash: Robust Hashing via L_infinity-norm Distortion	Amirali Aghazadeh, Andrew Lan, Anshumali Shrivastava, Richard Baraniuk	Inspired by recent progress in robust optimization, we develop a novel hashing algorithm, dubbed RHash, that instead minimizes the L_1-norm, worst-case distortion among pairs of points.
189	Grounding of Human Environments and Activities for Autonomous Robots	Muhannad Alomari, Paul Duckworth, Nils Bore, Majd Hawasly, David C. Hogg, Anthony G. Cohn	In this paper, we present a novel, online, incremental framework for unsupervised symbol grounding in real-world, human environments for autonomous robots.
190	Universal Reinforcement Learning Algorithms: Survey and Experiments	John Aslanides, Jan Leike, Marcus Hutter	We present a short and accessible survey of these URL algorithms under a unified notation and framework, along with results of some experiments that qualitatively illustrate some properties of the resulting policies, and their relative performance on partially-observable gridworld environments.
191	Bayesian Aggregation of Categorical Distributions with Applications in Crowdsourcing	Alexandry Augustin, Matteo Venanzi, Alex Rogers, Nicholas R. Jennings	For such cases, we provide a new Bayesian framework for aggregating these responses (expressed in the form of categorical distributions) that for the first time accounts for spammers.
192	Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions	Aijun Bai, Stuart Russell	In this paper, we propose a new hierarchical reinforcement learning algorithm that discovers such internal transitions automatically, and shortcircuits them recursively in computation of Q values.
193	Mining Convex Polygon Patterns with Formal Concept Analysis	Aimene Belfodil, Sergei O. Kuznetsov, Céline Robardet, Mehdi Kaytoue	We thus introduce convex polygons, a good trade-off for capturing high density areas in any pattern mining task.
194	Handling Noise in Boolean Matrix Factorization	Radim Belohlavek, Martin Trnecka	We present an experimental evaluation of several existing algorithms and compare the results to the observations available in the literature.
195	AccGenSVM: Selectively Transferring from Previous Hypotheses	Diana Benavides-Prado, Yun Sing Koh, Patricia Riddle	We introduce a novel method for selectively transferring elements from previous hypotheses learned with Support Vector Machines.
196	Unsupervised Learning of Deep Feature Representation for Clustering Egocentric Actions	Bharat Lal Bhatnagar, Suriya Singh, Chetan Arora, C.V. Jawahar	In this work, we propose a robust and generic unsupervised approach for first person action clustering.
197	Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning	Lidong Bing, William W. Cohen, Bhuwan Dhingra	We propose a general approach to modeling semi-supervised learning (SSL) algorithms.
198	Human-Centric Justification of Machine Learning Predictions	Or Biran, Kathleen McKeown	We propose a novel approach to producing justifications that is geared towards users without machine learning expertise, focusing on domain knowledge and on human reasoning, and utilizing natural language generation.
199	Context Attentive Bandits: Contextual Bandit with Restricted Context	Djallel Bouneffouf, Irina Rish, Guillermo Cecchi, Raphaël Féraud	This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling.Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context (TSRC) and the Windows Thompson Sampling with Restricted Context (WTSRC), for handling stationary and nonstationary environments, respectively.
200	SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation	Chenwei Cai, Ruining He, Julian McAuley	In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance.
201	Multiple-Weight Recurrent Neural Networks	Zhu Cao, Linlin Wang, Gerard de Melo	In this work, we solve this problem by proposing Multiple-Weight RNNs and LSTMs, which rely on multiple weight matrices in an attempt to mimic the human ability of switching between contexts.
202	Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time	Poo-Hee Chang, Ah-Hwee Tan	In this paper, we propose a computational model called STEM for encoding and recall of episodic events together with the associated contextual information in real time.
203	Data-driven Random Fourier Features using Stein Effect	Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos	In this paper, we propose a novel shrinkage estimator from “Stein effect”, which provides a data-driven weighting strategy for random features and enjoys theoretical justifications in terms of lowering the empirical risk.
204	Importance-Aware Semantic Segmentation for Autonomous Driving System	Bi-ke Chen, Chen Gong, Jian Yang	In this paper, we argue that existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safe-driving.
205	Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment	Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo	Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution.
206	Scalable Normalized Cut with Improved Spectral Rotation	Xiaojun Chen, Feiping Nie, Joshua Zhexue Huang, Min Yang	In this paper, we propose a Scalable Normalized Cut method for clustering of large scale data.
207	Semi-supervised Feature Selection via Rescaled Linear Regression	Xiaojun Chen, Guowen Yuan, Feiping Nie, Joshua Zhexue Huang	In this paper, we propose a novel semi-supervised feature selection method which can learn both global and sparse solution of the projection matrix.
208	Training Group Orthogonal Neural Networks with Privileged Information	Yunpeng Chen, Xiaojie Jin, Jiashi Feng, Shuicheng Yan	In this paper, we consider how to use privileged information to promote inherent diversity of a single CNN model such that the model can learn better representations and offer stronger generalization ability.
209	Projection Free Rank-Drop Steps	Edward Cheung, Yuying Li	To address this issue, we propose a rank-drop method for nuclear norm constrained problems.
210	End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks	Min-je Choi, Sehun Jeong, Hakjoo Oh, Jaegul Choo	In this paper, we propose a novel, data-driven approach that is completely end-to-end without requiring any hand-crafted features, thus free from any program language-specific structural limitations.
211	Optimal Feature Selection for Decision Robustness in Bayesian Networks	YooJung Choi, Adnan Darwiche, Guy Van den Broeck	We propose the first algorithm to compute this expected same-decision probability for general Bayesian network classifiers, based on compiling the network into a tractable circuit representation.
212	Stacked Similarity-Aware Autoencoders	Wenqing Chu, Deng Cai	In order to preserve the latent geometric information in the data, we propose the stacked similarity-aware autoencoders.
213	Further Results on Predicting Cognitive Abilities for Adaptive Visualizations	Cristina Conati, Sébastien Lallé, Md. Abed Rahman, Dereck Toker	In this paper, we contribute to previous work by extending the type of visualizations considered and the set of cognitive abilities that can be predicted from gaze data, thus providing evidence on the generality of these findings.
214	Analogy-preserving functions: A way to extend Boolean samples	Miguel Couceiro, Nicolas Hug, Henri Prade, Gilles Richard	In this paper, we consider the use of analogical reasoning, and more particularly of analogical proportions for extending training sets.
215	Real-Time Navigation in Classical Platform Games via Skill Reuse	Michael Dann, Fabio Zambetta, John Thangarajah	Motivated by human play, we introduce an approach that leverages not only abstract “skills”, but also knowledge of what those skills can and cannot achieve.
216	Disguise Adversarial Networks for Click-through Rate Prediction	Yue Deng, Yilin Shen, Hongxia Jin	We introduced an adversarial learning framework for improving CTR prediction in Ads recommendation.
217	Logic Tensor Networks for Semantic Image Interpretation	Ivan Donadello, Luciano Serafini, Artur d’Avila Garcez	In this paper, we develop and apply LTNs to two of the main tasks of SII, namely, the classification of an image’s bounding boxes and the detection of the relevant part-of relations between objects.
218	Autoencoder Regularized Network For Driving Style Representation Learning	Weishan Dong, Ting Yuan, Kai Yang, Changsheng Li, Shilei Zhang	In this paper, we study learning generalized driving style representations from automobile GPS trip data.
219	Privileged Matrix Factorization for Collaborative Filtering	Yali Du, Chang Xu, Dacheng Tao	To better explore the value of review comments, this paper presents the privileged matrix factorization method that utilize reviews in the learning of both user and item factors.
220	Collaborative Rating Allocation	Yali Du, Chang Xu, Dacheng Tao	Different from existing methods which treat each rating independently, we investigate the geometric properties of a user’s rating vector, and design a matrix completion method on the simplex.
221	Semi-Supervised Learning for Surface EMG-based Gesture Recognition	Yu Du, Yongkang Wong, Wenguang Jin, Wentao Wei, Yu Hu, Mohan Kankanhalli, Weidong Geng	In this work, we show that temporal relationship of sEMG signals and data glove provides implicit supervisory signal for learning the gesture recognition model.
222	Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation	Sebastijan Dumancic, Hendrik Blockeel	In this work we introduce an approach for relational unsupervised representation learning.
223	Learning to Learn Programs from Examples: Going Beyond Program Structure	Kevin Ellis, Sumit Gulwani	Our work here gives a machine learning approach for learning to learn programs that departs from previous work by relying upon features that are independent of the program structure, instead relying upon a learned bias over program behaviors, and more generally over program execution traces.
224	Top-k Supervise Feature Selection via ADMM for Integer Programming	Mingyu Fan, Xiaojun Chang, Xiaoqin Zhang, Di Wang, Liang Du	In this paper, we propose a novel supervised feature selection method to directly identify the top k features.
225	Improved Bounded Matrix Completion for Large-Scale Recommender Systems	Huang Fang, Zhang Zhen, Yiqun Shao, Cho-Jui Hsieh	In this paper, we focus on the idea of Bounded Matrix Completion (BMC) which imposes bounded constraint into the original matrix completion problem.
226	Object Detection Meets Knowledge Graphs	Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar	In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm.
227	Dynamic Multi-Task Learning with Convolutional Neural Network	Yuchun Fang, Zhengyan Ma, Zhaoxiang Zhang, Xu-Yao Zhang, Xiang Bai	In light of this, we propose a dynamic multi-task CNN model to handle these problems.
228	On the Complexity of Learning from Label Proportions	Benjamin Fish, Lev Reyzin	In this paper, we resolve foundational questions regarding the computational complexity of learning in this setting.
229	SVD-Based Screening for the Graphical Lasso	Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda	This paper proposes Sting, a fast approach to the graphical lasso.
230	Identifying Human Mobility via Trajectory Embeddings	Qiang Gao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Xucheng Luo, Fengli Zhang	We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL).
231	Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World	Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano	Specifically, we focus here on online dictionary learning (i.e. sparse linear autoencoder), and propose a simple but effective online model selection approach involving “birth” (addition) and “death” (removal) of hidden units representing dictionary elements, in response to changing inputs; we draw inspiration from the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with better adaptation to new environments.
232	Towards Understanding the Invertibility of Convolutional Neural Networks	Anna Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee	To understand this approximate invertibility phenomenon and how to leverage it more effectively, we focus on a theoretical explanation and develop a mathematical model of sparse signal recovery that is consistent with CNNs with random weights.
233	Sample Efficient Policy Search for Optimal Stopping Domains	Karan Goel, Christoph Dann, Emma Brunskill	We propose GFSE, a simple and flexible model-free policy search method that reuses data for sample efficiency by leveraging problem structure.
234	Extracting Visual Knowledge from the Web with Multimodal Learning	Dihong Gong, Daisy Zhe Wang	In this paper we present a multimodal learning algorithm to integrate text information into visual knowledge extraction.
235	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	Huifeng Guo, Ruiming TANG, Yunming Ye, Zhenguo Li, Xiuqiang He	In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions.
236	A Density-based Nonparametric Model for Online Event Discovery from the Social Media Data	Jinjin Guo, Zhiguo Gong	In this paper, we propose a novel online event discovery model DP-density to capture various events from the social media data.
237	Exclusivity Regularized Machine: A New Ensemble SVM Classifier	Xiaojie Guo, Xiaobo Wang, Haibin Ling	This paper defines a novel measurement of diversity, termed as exclusivity.
238	ROUTE: Robust Outlier Estimation for Low Rank Matrix Recovery	Xiaojie Guo, Zhouchen Lin	By considering the above, this paper designs a method for recovering the low rank matrix with robust outlier estimation, termed as ROUTE, in a unified manner.
239	Improved Deep Embedded Clustering with Local Structure Preservation	Xifeng Guo, Long Gao, Xinwang Liu, Jianping Yin	To address this issue, in this paper, we propose the Improved Deep Embedded Clustering (IDEC) algorithm to take care of data structure preservation.
240	Robust Asymmetric Bayesian Adaptive Matrix Factorization	Xin Guo, Boyuan Pan, Deng Cai, Xiaofei He	To address these problems, this paper assumes that the loss follows a mixture of Asymmetric Laplace distributions and proposes robust Asymmetric Laplace Adaptive Matrix Factorization model(ALAMF) under bayesian matrix factorization framework.
241	SitNet: Discrete Similarity Transfer Network for Zero-shot Hashing	Yuchen Guo, Guiguang Ding, Jungong Han, Yue Gao	In this paper, we propose a novel zero-shot hashing approach, called Discrete Similarity Transfer Network (SitNet), to preserve the semantic similarity between images from both “seen” concepts and new “unseen” concepts.
242	Synthesizing Samples for Zero-shot Learning	Yuchen Guo, Guiguang Ding, Jungong Han, Yue Gao	In this paper, we propose a novel approach which turns the ZSL problem into a conventional supervised learning problem by synthesizing samples for the unseen classes.
243	Understanding Users’ Budgets for Recommendation with Hierarchical Poisson Factorization	Yunhui Guo, Congfu Xu, Hanzhang Song, Xin Wang	In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users’ ratings and budgets.
244	Improved Strong Worst-case Upper Bounds for MDP Planning	Anchit Gupta, Shivaram Kalyanakrishnan	In this paper, we generalise a contrasting algorithm called the Fibonacci Seesaw, and derive a bound of poly(n, k) k^{0.6834n}.
245	Instability Prediction in Power Systems using Recurrent Neural Networks	Ankita Gupta, Gurunath Gurrala, Pidaparthy S Sastry	In this paper we present an interesting application of stacked Gated Recurrent Unit (GRU) based RNN for early prediction of imminent instability in a power system based on normal measurements of power system variables over time.
246	Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach	Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto	In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC: how to answer queries concerning test entities not observed at training time.
247	Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering	Junwei Han, Kai Xiong, Feiping Nie	To address these two problems simultaneously, in this paper, we propose a novel approach denoted by orthogonal and nonnegative graph reconstruction (ONGR) that scales linearly with the data size.
248	Self-paced Mixture of Regressions	Longfei Han, Dingwen Zhang, Dong Huang, Xiaojun Chang, Jun Ren, Senlin Luo, Junwei Han	In this paper, we make the earliest effort on Self-paced Learning (SPL) in MoR, i.e., Self-paced mixture of regressions (SPMoR) model.
249	Online Multitask Relative Similarity Learning	Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao	To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning).
250	Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel	Jia He, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long	This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework.
251	Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking	Jing He, Xin Li, Lejian Liao	In this paper, we propose a two-fold approach for next POI recommendation.
252	Storage Fit Learning with Unlabeled Data	Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou	In this paper, we focus on graph-based semi-supervised learning and propose two storage fit learning approaches which can adjust their behaviors to different storage budgets.
253	Fast Recursive Low-rank Tensor Learning for Regression	Ming Hou, Brahim Chaib-draa	In this work, we develop a fast sequential low-rank tensor regression framework, namely recursive higher-order partial least squares (RHOPLS).
254	Diversifying Personalized Recommendation with User-session Context	Liang Hu, Longbing Cao, Shoujin Wang, Guandong Xu, Jian Cao, Zhiping Gu	We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles.
255	Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization	Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, Bo Zhang	In this paper, we present an effective semi-supervised max-margin topic model by naturally introducing manifold posterior regularization to a regularized Bayesian topic model, named LapMedLDA.
256	Mention Recommendation for Twitter with End-to-end Memory Network	Haoran Huang, Qi Zhang, Xuanjing Huang	In this study, we investigated the problem of recommending usernames when people attempt to use the “@” sign to mention other people in twitter-like social media.
257	Cost-Effective Active Learning from Diverse Labelers	Sheng-Jun Huang, Jia-Lve Chen, Xin Mu, Zhi-Hua Zhou	In this paper, we perform active selection on both instances and labelers, aiming to improve the classification model most with the lowest cost.
258	Multi-instance multi-label active learning	Sheng-Jun Huang, Nengneng Gao, Songcan Chen	In this paper, we propose a MIML active learning algorithm, which exploits diversity and uncertainty in both the input and output space to query the most valuable information.
259	Cross-modal Common Representation Learning by Hybrid Transfer Network	Xin Huang, Yuxin Peng, Mingkuan Yuan	This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task.
260	Incremental Matrix Factorization: A Linear Feature Transformation Perspective	Xunpeng Huang, Le Wu, Enhong Chen, Hengshu Zhu, Qi Liu, Yijun Wang	In this paper, we propose a general incremental MF framework by designing a linear transformation of user and item latent vectors over time.
261	Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code	Xuan Huo, Ming Li	In this paper, we propose a novel model LS-CNN, which enhances the unified features by exploiting the sequential nature of source code.
262	Ordinal Zero-Shot Learning	Zengwei Huo, Xin Geng	This paper deals with zero-shot learning for ordinal classification.
263	Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks	Yasutoshi Ida, Yasuhiro Fujiwara, Sotetsu Iwamura	In this paper, we propose a novel adaptive learning rate algorithm called SDProp.
264	Privacy Issues Regarding the Application of DNNs to Activity-Recognition using Wearables and Its Countermeasures by Use of Adversarial Training	Yusuke Iwasawa, Kotaro Nakayama, Ikuko Yairi, Yutaka Matsuo	In this study, we analyzed the features learned by conventional deep neural networks when applied to data of wearables to confirm this phenomenon.Based on the results of our analysis, we propose the use of an adversarial training framework to suppress the risk of sensitive/unintended information disclosure.
265	Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning	Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao	This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning.
266	Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks	He Jiang, Yangqiu Song, Chenguang Wang, Ming Zhang, Yizhou Sun	In this paper, we present a semi-supervised learning algorithm constrained by the types of HINs.
267	Exploration of Tree-based Hierarchical Softmax for Recurrent Language Models	Nan Jiang, Wenge Rong, Min Gao, Yikang Shen, Zhang Xiong	As an alternative to gradient approximation and softmax with class decomposition, we explore the tree-based hierarchical softmax method and reform its architecture, making it compatible with modern GPUs and introducing a compact tree-based loss function.
268	Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning	Wenhao Jiang, Cheng Deng, Wei Liu, Feiping Nie, Fu-lai Chung, Heng Huang	In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation.
269	Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering	Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, Hanning Zhou	In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE).
270	Improving Classification Accuracy of Feedforward Neural Networks for Spiking Neuromorphic Chips	Antonio Jimeno Yepes, Jianbin Tang, Benjamin Scott Mashford	The main contribution of this paper is a new learning algorithm that learns a TrueNorth configuration ready for deployment.
271	Confusion Graph: Detecting Confusion Communities in Large Scale Image Classification	Ruochun Jin, Yong Dou, Yueqing Wang, Xin Niu	Based on this, we propose a graph-based tool named “confusion graph” to quantify these confusions and further reveal the community structure inside the database.
272	A Functional Dynamic Boltzmann Machine	Hiroshi Kajino	In this paper, we present a functional dynamic Boltzmann machine (F-DyBM) as a generative model of a functional time series.
273	Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression	Pasan Karunaratne, Masud Moshtaghi, Shanika Karunasekera, Aaron Harwood, Trevor Cohn	In this work we provide novel kernel-combination methods to explicitly model working-week effects in time-series data for more accurate predictions using Gaussian Process Regression.
274	Bernoulli Rank-1 Bandits for Click Feedback	Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Claire Vernade, Zheng Wen	In this paper we propose Rank1ElimKL, which replaces the crude confidence intervals of Rank1Elim with confidence intervals based on Kullback-Leibler (KL) divergences. We propose the learning problem of a Bernoulli rank-1 bandit where at each step, the learning agent chooses a pair of row and column arms, and receives the product of their Bernoulli-distributed values as a reward.
275	Learning deep structured network for weakly supervised change detection	Salman Khan, Xuming He, Fatih Porikli, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri	In this paper, we present a weakly supervised approach that needs only image-level labels to simultaneously detect and localize changes in a pair of images.
276	DeepStory: Video Story QA by Deep Embedded Memory Networks	Kyung-Min Kim, Min-Oh Heo, Seong-Ho Choi, Byoung-Tak Zhang	We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data.
277	Efficiency Through Procrastination: Approximately Optimal Algorithm Configuration with Runtime Guarantees	Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier	We address this gap with a new algorithm configuration framework, Structured Procrastination.
278	Decreasing Uncertainty in Planning with State Prediction	Senka Krivic, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sandor Szedmak, Justus Piater	In this paper we present an approach for predicting new information about a partially-known state.
279	Learning Latest Classifiers without Additional Labeled Data	Atsutoshi Kumagai, Tomoharu Iwata	In this paper, we propose a method to learn classifiers by using newly obtained unlabeled data, which are easy to prepare, as well as labeled data collected beforehand.
280	Earth Mover’s Distance Pooling over Siamese LSTMs for Automatic Short Answer Grading	Sachin Kumar, Soumen Chakrabarti, Shourya Roy	Here we introduce a novel framework for ASAG by cascading three neural building blocks: Siamese bidirectional LSTMs applied to a model and a student answer, a novel pooling layer based on earth-mover distance (EMD) across all hidden states from both LSTMs, and a flexible final regression layer to output scores.
281	Saliency Guided End-to-End Learning for Weakly Supervised Object Detection	Baisheng Lai, Xiaojin Gong	To address this issue, this paper integrates saliency into a deep architecture, in which the location information is explored both explicitly and implicitly.
282	Basket-Sensitive Personalized Item Recommendation	Duc-Trong Le, Hady W. Lauw, Yuan Fang	Towards this goal, we propose two approaches.
283	Learning Sparse Representations in Reinforcement Learning with Sparse Coding	Lei Le, Raksha Kumaraswamy, Martha White	In this work, we develop a supervised sparse coding objective for policy evaluation.
284	Semantic Visualization for Short Texts with Word Embeddings	Tuan M. V. Le, Hady W. Lauw	We propose a model called GaussianSV, which outperforms pipelined baselines that derive topic models and visualization coordinates as disjoint steps, as well as semantic visualization baselines that do not consider word embeddings.
285	Name Nationality Classification with Recurrent Neural Networks	Jinhyuk Lee, Hyunjae Kim, Miyoung Ko, Donghee Choi, Jaehoon Choi, Jaewoo Kang	We propose a recurrent neural network based model which predicts nationalities of each name using automatic feature extraction.
286	Constrained Bayesian Reinforcement Learning via Approximate Linear Programming	Jongmin Lee, Youngsoo Jang, Pascal Poupart, Kee-Eung Kim	In this paper, we consider the safe learning scenario where we need to restrict the exploratory behavior of a reinforcement learning agent.
287	High Dimensional Bayesian Optimization using Dropout	Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Alistair Shilton	We propose a new method for high-dimensional Bayesian optimization, that uses a drop-out strategy to optimize only a subset of variables at each iteration.
288	Effective Representing of Information Network by Variational Autoencoder	Hang Li, Haozheng Wang, Zhenglu Yang, Haochen Liu	The present study proposes a deep network representation model that seamlessly integrates the text information and structure of a network.
289	Self-paced Convolutional Neural Networks	Hao Li, Maoguo Gong	In order to distinguish the reliable data from the noisy and confusing data, we improve CNNs with self-paced learning (SPL) for enhancing the learning robustness of CNNs.
290	Learning User’s Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach	Huayu Li, Yong Ge, Defu Lian, Hao Liu	In this paper, we propose a novel unified approach that could effectively learn fine-grained and interpretable user’s interest, and adaptively model the missing data.
291	Efficient Kernel Selection via Spectral Analysis	Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang	In this paper, we propose a novel kernel selection criterion based on a newly defined spectral measure of a kernel matrix, with sound theoretical foundation and high computational efficiency.
292	Improving the Generalization Performance of Multi-class SVM via Angular Regularization	Jianxin Li, Haoyi Zhou, Pengtao Xie, Yingchun Zhang	In this paper, we introduce a new type of regularization approach — angular regularization, that encourages the coefficient vectors to have larger angles such that class regions can be widen to flexibly accommodate unseen samples.
293	Large-scale Subspace Clustering by Fast Regression Coding	Jun Li, Handong Zhao, Zhiqiang Tao, Yun Fu	To overcome this limitation, we propose a Fast Regression Coding (FRC) to optimize regression codes, and simultaneously train a non-linear function to approximate the codes.
294	Projective Low-rank Subspace Clustering via Learning Deep Encoder	Jun Li, Liu Hongfu, Handong Zhao, Yun Fu	To address this challenge, we create a projective low-rank subspace clustering (PLrSC) scheme for large scale clustering problem.
295	Radar: Residual Analysis for Anomaly Detection in Attributed Networks	Jundong Li, Harsh Dani, Xia Hu, Huan Liu	Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection.
296	Reconstruction-based Unsupervised Feature Selection: An Embedded Approach	Jundong Li, Jiliang Tang, Huan Liu	In this paper, we investigate how to learn the reconstruction function from the data automatically for unsupervised feature selection, and propose a novel reconstruction-based unsupervised feature selection framework REFS, which embeds the reconstruction function learning process into feature selection.
297	Multi-Stream Deep Similarity Learning Networks for Visual Tracking	Kunpeng Li, Yu Kong, Yun Fu	In this paper, we adopt a tracking-by-verification scheme to overcome these challenges by determining the patch in the subsequent frame that is most similar to the target template and distinctive to the background context.
298	Affinity Learning for Mixed Data Clustering	Nan Li, Longin Jan Latecki	In this paper, we propose a novel affinity learning based framework for mixed data clustering, which includes: how to process data with mixed-type attributes, how to learn affinities between data points, and how to exploit the learned affinities for clustering.
299	Online Robust Low-Rank Tensor Learning	Ping Li, Jiashi Feng, Xiaojie Jin, Luming Zhang, Xianghua Xu, Shuicheng Yan	In this paper, we propose an Online Robust Low-rank Tensor Modeling (ORLTM) approach to address these challenges.
300	Self-paced Compensatory Deep Boltzmann Machine for Semi-Structured Document Embedding	Shuangyin Li, Rong Pan, Jun Yan	In the paper, we introduce a Self-paced Compensatory Deep Boltzmann Machine (SCDBM) architecture that learns a deep neural network by using metadata information to learn deep structure layer-wisely for Semi-Structured Documents (SSDs) embedding in a self-paced way.
301	Person Re-Identification by Deep Joint Learning of Multi-Loss Classification	Wei Li, Xiatian Zhu, Shaogang Gong	In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context.
302	Locality Adaptive Discriminant Analysis	Xuelong Li, Mulin Chen, Feiping Nie, Qi Wang	In this paper, we propose a new supervised dimensionality reduction method, Locality Adaptive Discriminant Analysis (LADA), to lean a representative subspace of the data.
303	MAM-RNN: Multi-level Attention Model Based RNN for Video Captioning	Xuelong Li, Bin Zhao, Xiaoqiang Lu	In this paper, a Multi-level Attention Model based Recurrent Neural Network (MAM-RNN) is proposed, where MAM is utilized to encode the visual feature and RNN works as the decoder to generate the video caption.
304	Classification and Representation Joint Learning via Deep Networks	Ya Li, Xinmei Tian, Xu Shen, Dacheng Tao	In this paper, we propose a deep learning framework that considers both class label information and local spatial distribution information between training samples.
305	CFNN: Correlation Filter Neural Network for Visual Object Tracking	Yang Li, Zhan Xu, Jianke Zhu	To track single target in a wide range of videos, we present a novel Correlation Filter Neural Network architecture, as well as a complete visual tracking pipeline, The proposed approach is a special case of CNN, whose initialization does not need any pre-training on the external dataset.
306	Demystifying Neural Style Transfer	Yanghao Li, Naiyan Wang, Jiaying Liu, Xiaodi Hou	In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem.
307	End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification	Zheng Li, Yu Zhang, Ying Wei, Yuxiang Wu, Qiang Yang	To address the problem, we introduce an end-to-end Adversarial Memory Network (AMN) for cross-domain sentiment classification.
308	Integrating Specialized Classifiers Based on Continuous Time Markov Chain	Zhizhong Li, Dahua Lin	This work explores a novel approach.
309	Incomplete Attribute Learning with auxiliary labels	Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen	In this paper, we tackle the incompleteness nature of visual attributes by introducing auxiliary labels into a novel transductive learning framework.
310	LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks	Ankita Likhyani, Srikanta Bedathur, Deepak P	In this paper, we focus on the problem of location promotion and develop a model to quantify the influence specific to a location between a pair of users.
311	Discriminative Deep Hashing for Scalable Face Image Retrieval	Jie Lin, Zechao Li, Jinhui Tang	In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval.
312	Hybrid Neural Networks for Learning the Trend in Time Series	Tao Lin, Tian Guo, Karl Aberer	Inspired by the recent successes of neural networks, in this paper we propose TreNet, a novel end-to-end hybrid neural network to learn local and global contextual features for predicting the trend of time series.
313	Regional Concept Drift Detection and Density Synchronized Drift Adaptation	Anjin Liu, Yiliao Song, Guangquan Zhang, Jie Lu	To retrieve non-drifted information from suspended historical data, we propose a local drift degree (LDD) measurement that can continuously monitor regional density changes.
314	Deep Neural Networks for High Dimension, Low Sample Size Data	Bo Liu, Ying Wei, Yu Zhang, Qiang Yang	In this paper, we propose a DNN model tailored for the HDLSS data, named Deep Neural Pursuit (DNP).
315	Locally Linear Factorization Machines	Chenghao Liu, Teng Zhang, Peilin Zhao, Jun Zhou, Jianling Sun	In this work, we present a novel Locally Linear Factorization Machines (LLFM) which overcomes this limitation by exploring local coding technique.
316	DeepFacade: A Deep Learning Approach to Facade Parsing	Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi	In this paper, we propose a deep learning based method for segmenting a facade into semantic categories.
317	Semi-supervised Orthogonal Graph Embedding with Recursive Projections	Hanyang Liu, Junwei Han, Feiping Nie	This paper proposes a novel dimensionality reduction method, called the semi-supervised orthogonal graph embedding with recursive projections (SOGE).
318	Modeling Hebb Learning Rule for Unsupervised Learning	Jia Liu, Maoguo Gong, Qiguang Miao	This paper presents to model the Hebb learning rule and proposes a neuron learning machine (NLM).
319	Fast SVM Trained by Divide-and-Conquer Anchors	Meng Liu, Chang Xu, Chao Xu, Dacheng Tao	In this paper, we propose to choose the representative points which are noted as anchors obtained from non-negative matrix factorization (NMF) in a divide-and-conquer framework, and then use the anchors to train an approximate SVM.
320	Improving Learning-from-Crowds through Expert Validation	Mengchen Liu, Liu Jiang, Junlin Liu, Xiting Wang, Jun Zhu, Shixia Liu	This paper introduces a semi-supervised learning algorithm that is capable of selecting the most informative instances and maximizing the influence of expert labels.
321	Accelerated Local Anomaly Detection via Resolving Attributed Networks	Ninghao Liu, Xiao Huang, Xia Hu	Motivated by the observations, in this paper, we propose to study the problem of effective and efficient local anomaly detection in attributed networks.
322	Cause-Effect Knowledge Acquisition and Neural Association Model for Solving A Set of Winograd Schema Problems	Quan Liu, Hui Jiang, Andrew Evdokimov, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, Yu Hu	This paper focuses on the investigations in Winograd Schema (WS), a challenging problem which has been proposed for measuring progress in commonsense reasoning.Due to the lack of commonsense knowledge and training data, very little work has been found on the WS problems in recent years.Actually, there is no shortcut to solve this problem except to collect more commonsense knowledge and design suitable models.Therefore, this paper addresses a set of WS problems by proposing a knowledge acquisition method and a general neural association model.To avoid the sparseness issue, the knowledge we aim to collect is the cause-effect relationships between thousands of commonly used words.The knowledge acquisition method supports us to extract hundreds of thousands of cause-effect pairs from large text corpus automatically.Meanwhile, a neural association model (NAM) is proposed to encode the association relationships between any two discrete events.Based on the extracted knowledge and the NAM models, in this paper, we successfully build a system for solving WS problems from scratch and achieve 70.0% accuracy.Most importantly, this paper provides a flexible framework to solve WS problems based on event association and neural network methods.
323	Learning Concise Representations of Users’ Influences through Online Behaviors	Shenghua Liu, Houdong Zheng, Huawei Shen, Xueqi Cheng, Xiangwen Liao	Thus we propose a model that defines parameters for every user with a latent influence vector and a susceptibility vector.
324	Adaptive Group Sparse Multi-task Learning via Trace Lasso	Sulin Liu, Sinno Jialin Pan	In this paper, we propose a new MTL method that can adaptively group correlated tasks into clusters and share information among the correlated tasks only.
325	Understanding How Feature Structure Transfers in Transfer Learning	Tongliang Liu, Qiang Yang, Dacheng Tao	In this paper, motivated by self-taught learning, we regard a set of bases as a feature structure of a domain if the bases can (approximately) reconstruct any observation in this domain.
326	Deep Ordinal Regression Based on Data Relationship for Small Datasets	Yanzhu Liu, Adams Wai Kin Kong, Chi Keong Goh	This paper proposes a new approach which transforms the ordinal regression problem to binary classification problems and uses triplets with instances from different categories to train deep neural networks such that high-level features describing their ordinal relationship can be extracted automatically.
327	Learning User Dependencies for Recommendation	Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Lixin Duan, Xiao-Li Li	In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which can automatically learn the dependencies between users to improve recommendation accuracy.
328	JM-Net and Cluster-SVM for Aerial Scene Classification	Xiaoqiang Lu, Yuan Yuan, Jie Fang	To address the problem, a novel convolutional neural network named JM-Net is proposed in this paper, which has different size of convolution kernels in same layer and ignores the fully convolytion layer, so it has fewer parameters and can be trained well on aerial datasets.
329	Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift	Yang Lu, Yiu-ming Cheung, Yuan Yan Tang	In this paper, we propose a chunk-based incremental learning method called Dynamic Weighted Majority for Imbalance Learning (DWMIL) to deal with the data streams with concept drift and class imbalance problem.
330	Sampling for Approximate Maximum Search in Factorized Tensor	Zhi Lu, Yang Hu, Bing Zeng	In this work, we propose a sampling-based approach for finding the top entries of a tensor which is decomposed by the CANDECOMP/PARAFAC model.
331	Image Matching via Loopy RNN	Donghao Luo, Bingbing Ni, Yichao Yan, Xiaokang Yang	Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration.
332	Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process	Ling Luo, Bin Li, Irena Koprinska, Shlomo Berkovsky, Fang Chen	We propose FC-CSM, a Customer Segmentation Model based on a Fragmentation-Coagulation process, which can track the evolution of customer segmentation, including the splitting and merging of customer groups.
333	Adaptive Semi-Supervised Learning with Discriminative Least Squares Regression	Minnan Luo, Lingling Zhang, Feiping Nie, Xiaojun Chang, Buyue Qian, Qinghua Zheng	In this paper, we distinguish the label fitting of labeled and unlabeled training data through a probabilistic vector with an adaptive parameter, which always ensures the significant importance of labeled data and characterizes the contribution of unlabeled instance according to its uncertainty.
334	EigenNet: Towards Fast and Structural Learning of Deep Neural Networks	Ping Luo	We address these two issues by introducing EigenNet, an architecture that not only accelerates training but also adjusts number of hidden neurons to reduce over-fitting.
335	Symmetric Non-negative Latent Factor Models for Undirected Large Networks	Xin Luo, Ming-Sheng Shang	Non-negative latent factor (NLF) models have proven to be effective and efficient in acquiring useful knowledge from asymmetric networks.
336	Exploiting High-Order Information in Heterogeneous Multi-Task Feature Learning	Yong Luo, Dacheng Tao, Yonggang Wen	We therefore develop a tensor based heterogeneous MTFL (THMTFL) framework to exploit such high-order information.
337	General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer	Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao	We therefore develop a general and flexible heterogeneous TDML (HTDML) framework based on the knowledge fragment transfer strategy.
338	WALKING WALKing walking: Action Recognition from Action Echoes	Qianli Ma, Lifeng Shen, Enhuan Chen, Shuai Tian, Jiabing Wang, Garrison W. Cottrell	In this paper, the 3D skeleton sequences are regarded as multivariate time series, and their dynamics and multiscale features are efficiently learned from action echo states.
339	Cross-Domain Recommendation: An Embedding and Mapping Approach	Tong Man, Huawei Shen, Xiaolong Jin, Xueqi Cheng	In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR.
340	Count-Based Exploration in Feature Space for Reinforcement Learning	Jarryd Martin, Suraj Narayanan S., Tom Everitt, Marcus Hutter	We introduce a new count-based optimistic exploration algorithm for Reinforcement Learning (RL) that is feasible in environments with high-dimensional state-action spaces.
341	Exemplar-centered Supervised Shallow Parametric Data Embedding	Martin Renqiang Min, Hongyu Guo, Dongjin Song	To effectively solve these issues, we present an exemplar-centered supervised shallow parametric data embedding model, using a Maximally Collapsing Metric Learning (MCML) objective.
342	Logistic Markov Decision Processes	Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu	In this work, we consider the long-term decision problem associated with user interaction.
343	Beyond the Nystrom Approximation: Speeding up Spectral Clustering using Uniform Sampling and Weighted Kernel k-means	Mahesh Mohan, Claire Monteleoni	In this paper we present a framework for spectral clustering based on the following simple scheme: sample a subset of the input points, compute the clusters for the sampled subset using weighted kernel k-means (Dhillon et al. 2004) and use the resulting centers to compute a clustering for the remaining data points.
344	Rescale-Invariant SVM for Binary Classification	Mojtaba Montazery, Nic Wilson	We define a more robust decision-making approach for binary classification, in which one sample strongly belongs to a class if it belongs to that class for all possible rescalings of features.
345	Completely Heterogeneous Transfer Learning with Attention – What And What Not To Transfer	Seungwhan Moon, Jaime Carbonell	We study a transfer learning framework where source and target datasets are heterogeneous in both feature and label spaces.
346	Thresholding Bandits with Augmented UCB	Subhojyoti Mukherjee, Naveen Kolar Purushothama, Nandan Sudarsanam, Balaraman Ravindran	In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold.
347	Self-Paced Multitask Learning with Shared Knowledge	Keerthiram Murugesan, Jaime Carbonell	This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning.
348	Learning Feature Engineering for Classification	Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B. Khalil, Deepak Turaga	We present a novel technique, called Learning Feature Engineering (LFE), for automating feature engineering in classification tasks.
349	Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning	Sanmit Narvekar, Jivko Sinapov, Peter Stone	In this paper, we formulate the design of a curriculum as a Markov Decision Process, which directly models the accumulation of knowledge as an agent interacts with tasks, and propose a method that approximates an execution of an optimal policy in this MDP to produce an agent-specific curriculum.
350	Large-scale Online Kernel Learning with Random Feature Reparameterization	Tu Dinh Nguyen, Trung Le, Hung Bui, Dinh Phung	We develop a well-founded underlying theory for our method, including a general way to reparameterize the kernel, and a new tighter error bound on the approximation quality.
351	Discriminative Bayesian Nonparametric Clustering	Vu Nguyen, Dinh Phung, Trung Le, Hung Bui	We propose a general framework for discriminative Bayesian nonparametric clustering to promote the inter-discrimination among the learned clusters in a fully Bayesian nonparametric (BNP) manner.
352	Joint Capped Norms Minimization for Robust Matrix Recovery	Feiping Nie, Zhouyuan Huo, Heng Huang	In this paper, we propose a new robust matrix recovery model to address the above two challenges.
353	Self-weighted Multiview Clustering with Multiple Graphs	Feiping Nie, Jing Li, Xuelong Li	In this paper, we address this problem by exploring a Laplacian rank constrained graph, which can be approximately as the centroid of the built graph for each view with different confidences.
354	SEVEN: Deep Semi-supervised Verification Networks	Vahid Noroozi, Lei Zheng, Sara Bahaadini, Sihong Xie, Philip S. Yu	We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges.
355	Discovering Relevance-Dependent Bicluster Structure from Relational Data	Iku Ohama, Takuya Kida, Hiroki Arimura	In this paper, we propose a statistical model for relevance-dependent biclustering to analyze relational data.
356	Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection	Guansong Pang, Longbing Cao, Ling Chen, Huan Liu	This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors.
357	Flexible Orthogonal Neighborhood Preserving Embedding	Tianji Pang, Feiping Nie, Junwei Han	In this paper, we propose a novel linear subspace learning algorithm called Flexible Orthogonal Neighborhood Preserving Embedding (FONPE), which is a linear approximation of Locally Linear Embedding (LLE) algorithm.
358	Boosted Zero-Shot Learning with Semantic Correlation Regularization	Te Pi, Xi Li, Zhongfei (Mark) Zhang	With SCR embedded in the boosting objective, and with a self-controlled sample selection for learning robustness, we propose a unified framework, Boosted Zero-shot classification with Semantic Correlation Regularization (BZ-SCR).
359	Optimizing Ratio of Monotone Set Functions	Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou	If more time can be spent, we present the PORM algorithm, an anytime randomized iterative approach minimizing $f$ and $\textendash g$ simultaneously.
360	On Subset Selection with General Cost Constraints	Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang	This paper considers the subset selection problem with a monotone objective function and a monotone cost constraint, which relaxes the submodular property of previous studies.
361	Improving Stochastic Block Models by Incorporating Power-Law Degree Characteristic	Maoying Qiao, Jun Yu, Wei Bian, Qiang Li, Dacheng Tao	To address this issue, we introduce degree decay variables into SBM, termed power-law degree SBM (PLD-SBM), to model the varying probability of connections between node pairs.
362	A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction	Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, Garrison W. Cottrell	In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues.
363	Stacking With Auxiliary Features	Nazneen Fatema Rajani, Raymond J. Mooney	In this paper, we propose stacking with auxiliary features that learns to fuse additional relevant information from multiple component systems as well as input instances to improve performance.
364	Robust Softmax Regression for Multi-class Classification with Self-Paced Learning	Yazhou Ren, Peng Zhao, Yongpan Sheng, Dezhong Yao, Zenglin Xu	To address this issue, we propose a model of robust softmax regression (RoSR) originated from the self-paced learning (SPL) paradigm for multi-class classification.
365	Sense Beauty by Label Distribution Learning	Yi Ren, Xin Geng	This paper presents an approach to learning the human sense toward facial beauty.
366	Distributed Accelerated Proximal Coordinate Gradient Methods	Yong Ren, Jun Zhu	We develop a general accelerated proximal coordinate descent algorithm in distributed settings (Dis- APCG) for the optimization problem that minimizes the sum of two convex functions: the first part f is smooth with a gradient oracle, and the other one Ψ is separable with respect to blocks of coordinate and has a simple known structure (e.g., L1 norm).
367	Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations	Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez	We propose a method to explain the predictions of any differentiable model via the gradient of the class label with respect to the input (which provides a normal to the decision boundary).
368	See without looking: joint visualization of sensitive multi-site datasets	Debbrata K. Saha, Vince D. Calhoun, Sandeep R. Panta, Sergey M. Plis	This paper introduces an algorithm to solve this problem: decentralized data stochastic neighbor embedding (dSNE).
369	LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity	Bidisha Samanta, Abir De, Abhijnan Chakraborty, Niloy Ganguly	In this paper, we propose Large Margin Point Process (LMPP), a probabilistic framework that integrates hashtag-tweet influence and hashtag-hashtag competitions, the two factors which play important roles in hashtag propagation.
370	Convolutional-Match Networks for Question Answering	Spyridon Samothrakis, Tom Vodopivec, Michael Fairbank, Maria Fasli	In this paper, we present a simple, yet effective, attention and memory mechanism that is reminiscent of Memory Networks and we demonstrate it in question-answering scenarios.
371	Locally Consistent Bayesian Network Scores for Multi-Relational Data	Oliver Schulte, Sajjad Gholami	We describe a new method that upgrades for multi-relational databases, a log-linear BN score designed for single-table i.i.d. data.
372	Compressed Nonparametric Language Modelling	Ehsan Shareghi, Gholamreza Haffari, Trevor Cohn	In this work we propose a novel framework which represents the HPYP model compactly using compressed suffix trees.
373	Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation	Tong Shen, Guosheng Lin, Chunhua Shen, Ian Reid	We address this issue by proposing a dense multi-label network module that is able to encourage the region consistency at different levels.
374	Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization	Zebang Shen, Hui Qian, Tongzhou Mu, Chao Zhang	In this paper, we propose a doubly stochastic algorithm with a novel accelerating multi-momentum technique to solve large scale empirical risk minimization problem for learning tasks.
375	Learning with Previously Unseen Features	Yuan Shi, Craig A. Knoblock	To effectively use new features, we propose a novel approach that learns a model over both the original and new features, with the goal of making the joint distribution of features and predicted labels similar to that in the training set.
376	Learning Hedonic Games	Jakub Sliwinski, Yair Zick	We introduce the notion of PAC stability – the equivalent of core stability under uncertainty – and examine the PAC stabilizability and learnability of several popular classes of hedonic games.
377	Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning	Jingkuan Song, Lianli Gao, Zhao Guo, Wu Liu, Dongxiang Zhang, Heng Tao Shen	Recent progress has been made in using attention based encoder-decoder framework for video captioning.
378	Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness	Kaisong Song, Wei Gao, Shi Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang	In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation.
379	Two dimensional Large Margin Nearest Neighbor for Matrix Classification	Kun Song, Feiping Nie, Junwei Han	In this paper, we propose a novel distance metric learning method named two dimensional large margin nearest neighbor (2DLMNNN), for improving the performance of k nearest neighbor (KNN) classifier in matrix classification.
380	Fast Sparse Gaussian Markov Random Fields Learning Based on Cholesky Factorization	Ivan Stojkovic, Vladisav Jelisavcic, Veljko Milutinovic, Zoran Obradovic	We propose a novel objective with a regularization term which penalizes an approximate product of the Cholesky decomposed precision matrix.
381	End-to-end optimization of goal-driven and visually grounded dialogue systems	Florian Strub, Harm de Vries, Jérémie Mary, Bilal Piot, Aaron Courville, Olivier Pietquin	In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues, based on the policy gradient algorithm.
382	Forecast the Plausible Paths in Crowd Scenes	Hang Su, Jun Zhu, Yinpeng Dong, Bo Zhang	To address these issues, we propose to explore the inherent crowd dynamics via a social-aware recurrent Gaussian process model, which facilitates the path prediction by taking advantages of the interplay between the rich prior knowledge and motion uncertainties.
383	Vertex-Weighted Hypergraph Learning for Multi-View Object Classification	Lifan Su, Yue Gao, Xibin Zhao, Hai Wan, Ming Gu, Jiaguang Sun	In this work, we propose to employ the hypergraph structure to formulate the relationship among 3D objects, taking the advantage of hypergraph on high-order correlation modelling.
384	Deep Supervised Hashing with Nonlinear Projections	Sen Su, Gang Chen, Xiang Cheng, Rong Bi	To improve the performance of deep hashing methods by generalizing projection functions, we propose the idea of implementing a pure nonlinear deep hashing network architecture.
385	Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces	Yanan Sui, Joel W. Burdick	We propose an efficient algorithm CorrDuel for the problem which makes decisions to simultaneously deliver effective therapy and explore the decision space.
386	CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis	Adam Summerville, Joseph Osborn, Michael Mateas	We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system.Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates \textit{causal} guard conditions which trigger transitions between modes.Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selction to penalize over-fitting and (2)~to determine the likely causes of each transition.CHARDA is easily extended with different classes of model templates, fitting methods, or predicates.In our experiments on a complex videogame character, CHARDA successfully discovers a reasonable over-approximation of the character’s true behaviors.Our results also compare favorably against recent work in automatically learning probabilistic timed automata in an aircraft domain: CHARDA exactly learns the modes of these simpler automata.
387	MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation	Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, Chi Xu	We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking.
388	Bayesian Dynamic Mode Decomposition	Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi	In this paper, we propose Bayesian DMD, which provides a principled way to transfer the advantages of the Bayesian formulation into DMD.
389	Student-t Process Regression with Student-t Likelihood	Qingtao Tang, Li Niu, Yisen Wang, Tao Dai, Wangpeng An, Jianfei Cai, Shu-Tao Xia	In contrast, in this work, we aim to handle both the target outliers and the input outliers at the same time.
390	Robust Survey Aggregation with Student-t Distribution and Sparse Representation	Qingtao Tang, Tao Dai, Li Niu, Yisen Wang, Shu-Tao Xia, Jianfei Cai	To address this issue, we propose a robust survey aggregation method based on Student-t distribution and sparse representation.
391	Inverse Covariance Estimation with Structured Groups	Shaozhe Tao, Yifan Sun, Daniel Boley	We propose a new estimator for this problem setting that can be derived efficiently via the conditional gradient method, leveraging chordal decomposition theory for scalability.
392	From Ensemble Clustering to Multi-View Clustering	Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, Yun Fu	To overcome this problem, we propose a novel Multi-View Ensemble Clustering (MVEC) framework to solve MVC in an Ensemble Clustering (EC) way, which generates Basic Partitions (BPs) for each view individually and seeks for a consensus partition among all the BPs.
393	Retaining Data from Streams of Social Platforms with Minimal Regret	Nguyen Thanh Tam, Matthias Weidlich, Duong Chi Thang, Hongzhi Yin, Nguyen Quoc Viet Hung	In this paper, we propose techniques to effectively decide which data to retain, such that the induced loss of information, the regret of neglecting certain data, is minimized.
394	Disambiguating Energy Disaggregation: A Collective Probabilistic Approach	Sabina Tomkins, Jay Pujara, Lise Getoor	We introduce a probabilistic framework which infers the energy consumption of individual appliances using a hinge-loss Markov random field (HL-MRF), which admits highly scalable inference.
395	TransNet: Translation-Based Network Representation Learning for Social Relation Extraction	Cunchao Tu, Zhengyan Zhang, Zhiyuan Liu, Maosong Sun	In this work, we present a novel Translation-based NRL model, TransNet, by regarding the interactions between vertices as a translation operation.
396	COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints	Toon Van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel	We propose COBRA, an active method that first over-clusters the data by running K-means with a $K$ that is intended to be too large, and subsequently merges the resulting small clusters into larger ones based on pairwise constraints.
397	Scaling Active Search using Linear Similarity Functions	Sibi Venkatesan, James K. Miller, Jeff Schneider, Artur Dubrawski	In this paper, we consider the problem of Active Search where we are given a similarity function between data points.
398	Sifting Common Information from Many Variables	Greg Ver Steeg, Shuyang Gao, Kyle Reing, Aram Galstyan	We leverage the recently introduced information sieve decomposition to formulate an incremental version of the common information problem that admits a simple fixed point solution, fast convergence, and complexity that is linear in the number of variables.
399	Locality Preserving Projections for Grassmann manifold	Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Haoran Chen, Muhammad Ali, Baocai Yin	In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion.
400	Tag Disentangled Generative Adversarial Network for Object Image Re-rendering	Chaoyue Wang, Chaohui Wang, Chang Xu, Dacheng Tao	In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination, expression, etc.).
401	Active Learning for Black-Box Semantic Role Labeling with Neural Factors	Chenguang Wang, Laura Chiticariu, Yunyao Li	In this paper, we present an active learning framework for black-box SRL models (i.e., models whose details are unknown).
402	Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification	Jin Wang, Zhongyuan Wang, Dawei Zhang, Jun Yan	In this paper, we propose a framework based on convolutional neural networks that combines explicit and implicit representations of short text for classification.
403	Multi-Component Nonnegative Matrix Factorization	Jing Wang, Feng Tian, Xiao Wang, Hongchuan Yu, Chang Hong Liu, Liang Yang	To overcome this limitation, we propose a novel multi-component nonnegative matrix factorization (MCNMF).
404	Convolutional 2D LDA for Nonlinear Dimensionality Reduction	Qi Wang, Zequn Qin, Feiping Nie, Yuan Yuan	In this paper, we propose a novel convolutional two-dimensional linear discriminant analysis (2D LDA) method for data representation.
405	Angle Principal Component Analysis	Qianqian Wang, Quanxue Gao, Xinbo Gao, Feiping Nie	To solve Angle PCA, we propose an iterative algorithm, which has closed-form solution in each iteration.
406	Instance-Level Label Propagation with Multi-Instance Learning	Qifan Wang, Gal Chechik, Chen Sun, Bin Shen	This paper proposes a novel Instance-Level Label Propagation (ILLP) approach that integrates label propagation with multi-instance learning.
407	A Sequence Labeling Convolutional Network and Its Application to Handwritten String Recognition	Qingqing Wang, Yue Lu	In this paper, we propose a sequence labeling convolutional network for the recognition of handwritten strings, in particular, the connected patterns.
408	Interactive Image Segmentation via Pairwise Likelihood Learning	Tao Wang, Quansen Sun, Qi Ge, Zexuan Ji, Qiang Chen, Guiyu Xia	This paper presents an interactive image segmentation approach where the segmentation problem is formulated as a probabilistic estimation manner.
409	Obtaining High-Quality Label by Distinguishing between Easy and Hard Items in Crowdsourcing	Wei Wang, Xiang-Yu Guo, Shao-Yuan Li, Yuan Jiang, Zhi-Hua Zhou	The experimental results demonstrate that the proposed approach by learning to distinguish between easy and hard items can significantly improve the label quality. In this paper, we study the problem of obtaining high-quality labels from the crowd and present an approach of learning the difficulty of items in crowdsourcing, in which we construct a small training set of items with estimated difficulty and then learn a model to predict the difficulty of future items.
410	Multiple Medoids based Multi-view Relational Fuzzy Clustering with Minimax Optimization	Yangtao Wang, Lihui Chen, Xiao-Li Li	In this paper, a new multi-view fuzzy clustering approach based on multiple medoids and minimax optimization called M4-FC for relational data is proposed.
411	App Download Forecasting: An Evolutionary Hierarchical Competition Approach	Yingzi Wang, Nicholas Jing Yuan, Yu Sun, Chuan Qin, Xing Xie	To address these problems, we propose the Evolutionary Hierarchical Competition Model (EHCM), which effectively considers the time-evolving multi-level competition among products.
412	Cascade Dynamics Modeling with Attention-based Recurrent Neural Network	Yongqing Wang, Huawei Shen, Shenghua Liu, Jinhua Gao, Xueqi Cheng	In this paper, we propose to an attention-based RNN to capture the cross-dependence in cascade.
413	Fast Change Point Detection on Dynamic Social Networks	Yu Wang, Aniket Chakrabarti, David Sivakoff, Srinivasan Parthasarathy	In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model.
414	Multiple Kernel Clustering Framework with Improved Kernels	Yueqing Wang, Xinwang Liu, Yong Dou, Rongchun Li	To address this issue, we propose a simple while effective framework to adaptively improve the quality of these base kernels.
415	Approximate Large-scale Multiple Kernel k-means Using Deep Neural Network	Yueqing Wang, Xinwang Liu, Yong Dou, Rongchun Li	In this paper, we propose an approximate algorithm to overcome these issues, and to make it be applicable to large-scale applications.
416	On Gleaning Knowledge from Multiple Domains for Active Learning	Zengmao Wang, Bo Du, Lefei Zhang, Liangpei Zhang, Ruimin Hu, Dacheng Tao	In this paper, a framework that attempts to glean knowledge from multiple domains for active learning by querying the most uncertain and representative samples from the target domain and calculating the importance weights for re-weighting the source data in a single unified formulation is proposed.
417	Doubly Sparsifying Network	Zhangyang Wang, Shuai Huang, Jiayu Zhou, Thomas S. Huang	We propose the doubly sparsifying network (DSN), by drawing inspirations from the double sparsity model for dictionary learning.
418	Improving Reinforcement Learning with Confidence-Based Demonstrations	Zhaodong Wang, Matthew E. Taylor	The key contribution of this work is to show that leveraging the target agent’s uncertainty in the source agent’s policy can significantly improve learning in two complex simulated domains, Keepaway and Mario.
419	Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code	Huihui Wei, Ming Li	In this paper, we address the software functional clone detection problem by learning supervised deep features.
420	Group-wise Deep Co-saliency Detection	Lina Wei, Shanshan Zhao, Omar El Farouk Bourahla, Xi Li, Fei Wu	In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output.
421	Deep Descriptor Transforming for Image Co-Localization	Xiu-Shen Wei, Chen-Lin Zhang, Yao Li, Chen-Wei Xie, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou	In this paper, we focus on the reusability of pre-trained deep convolutional models.
422	Learning from Demonstrations with High-Level Side Information	Min Wen, Ivan Papusha, Ufuk Topcu	In this work, we show that side information, when explicitly taken into account, indeed improves the performance and safety of the learned policy with respect to task implementation.
423	Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks	Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, Tao Mei	To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account.
424	Discriminant Tensor Dictionary Learning with Neighbor Uncorrelation for Image Set Based Classification	Fei Wu, Xiao-Yuan Jing, Wangmeng Zuo, Ruiping Wang, Xiaoke Zhu	In this paper, we utilize tensor to model an image set with two spatial modes and one set mode, which can fully explore the intrinsic structure of image set.
425	Unsupervised Deep Video Hashing with Balanced Rotation	Gengshen Wu, Li Liu, Yuchen Guo, Guiguang Ding, Jungong Han, Jialie Shen, Ling Shao	In this paper, an end-to-end hashing framework, namely Unsupervised Deep Video Hashing (UDVH), is proposed, where feature extraction, balanced code learning and hash function learning are integrated and optimized in a self-taught manner.
426	Modeling Trajectories with Recurrent Neural Networks	Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang	Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to address the constraints of topological structure on trajectory modeling.
427	Deep Context: A Neural Language Model for Large-scale Networked Documents	Hao Wu, Kristina Lerman	We propose a scalable neural language model that leverages the links between documents to learn the deep context of documents.
428	Sequence Prediction with Unlabeled Data by Reward Function Learning	Lijun Wu, Li Zhao, Tao Qin, Jianhuang Lai, Tie-Yan Liu	In this paper, we extend existing RL methods for sequence prediction to exploit unlabeled data.
429	Query-Driven Discovery of Anomalous Subgraphs in Attributed Graphs	Nannan Wu, Feng Chen, Jianxin Li, Jinpeng Huai, Bo Li	We present a novel, efficient approach for optimizing a generic nonlinear cost function subject to a query-specific structural constraint.
430	Dual Inference for Machine Learning	Yingce Xia, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu	In this paper, we propose a general framework of dual inference which can take advantage of both existing models from two dual tasks, without re-training, to conduct inference for one individual task.
431	Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, Tat-Seng Chua	In this work, we improve FM by discriminating the importance of different feature interactions.
432	SVD-free Convex-Concave Approaches for Nuclear Norm Regularization	Yichi Xiao, Zhe Li, Tianbao Yang, Lijun Zhang	In this paper, we study the general setting where the convex function could be non-smooth.
433	Dynamic Multi-View Hashing for Online Image Retrieval	Liang Xie, Jialie Shen, Jungong Han, Lei Zhu, Ling Shao	In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image.
434	Image-embodied Knowledge Representation Learning	Ruobing Xie, Zhiyuan Liu, Huanbo Luan, Maosong Sun	In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images.
435	Linear Manifold Regularization with Adaptive Graph for Semi-supervised Dimensionality Reduction	Kai Xiong, Feiping Nie, Junwei Han	To overcome the drawbacks, in this paper, we propose a novel approach called linear manifold regularization with adaptive graph (LMRAG) for semi-supervised dimensionality reduction.
436	Multi-Class Support Vector Machine via Maximizing Multi-Class Margins	Jie Xu, Xianglong Liu, Zhouyuan Huo, Cheng Deng, Feiping Nie, Heng Huang	In this paper, we propose a multi-class SVM model from the perspective of maximizing margin between training points and hyperplane, and analyze the relation between our model and other related methods.
437	Multi-view Feature Learning with Discriminative Regularization	Jinglin Xu, Junwei Han, Feiping Nie	To address these challenges, this paper proposes a novel multi-view feature learning framework, which is regularized by discriminative information and obtains a feature learning model that contains multiple discriminative feature weighting matrices for different views, and then yields multiple low dimensional features used for subsequent multi-view clustering.
438	Feature Selection via Scaling Factor Integrated Multi-Class Support Vector Machines	Jinglin Xu, Feiping Nie, Junwei Han	In this paper, we propose a novel feature selection method based on multi-class SVM, which introduces the scaling factor with a flexible parameter to renewedly adjust the distribution of feature weights and select the most discriminative features.
439	Incomplete Label Distribution Learning	Miao Xu, Zhi-Hua Zhou	In this paper, we will solve LDL problem when given \emph{incomplete} supervised information.
440	Multi-Positive and Unlabeled Learning	Yixing Xu, Chang Xu, Chao Xu, Dacheng Tao	Here we propose a one-step method that directly enables multi-class model to be trained using the given input multi-class data and that predicts the label based on the model decision.
441	Stochastic Online Anomaly Analysis for Streaming Time Series	Zhao Xu, Kristian Kersting, Lorenzo von Ritter	In this paper, we present an online nonparametric Bayesian method OLAD for anomaly analysis in streaming time series.
442	Tag-Aware Personalized Recommendation Using a Hybrid Deep Model	Zhenghua Xu, Thomas Lukasiewicz, Cheng Chen, Yishu Miao, Xiangwu Meng	In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized).
443	Deep Matrix Factorization Models for Recommender Systems	Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, Jiajun Chen	In this paper, we propose a novel matrix factorization model with neural network architecture.
444	Multiple Indefinite Kernel Learning for Feature Selection	Hui Xue, Yu Song, Hai-Ming Xu	In this paper, we propose a novel multiple indefinite kernel feature selection method (MIK-FS) based on the primal framework of indefinite kernel support vector machine (IKSVM), which applies an indefinite base kernel for each feature and then exerts an l1-norm constraint on kernel combination coefficients to select features automatically.
445	Tensor Decomposition with Missing Indices	Yuto Yamaguchi, Kohei Hayashi	How can we decompose a data tensor if the indices are partially missing?Tensor decomposition is a fundamental tool to analyze the tensor data.Suppose, for example, we have a 3rd-order tensor X where each element Xijk takes 1 if user i posts word j at location k on Twitter.Standard tensor decomposition expects all the indices are observed but, in some tweets, location k can be missing.In this paper, we study a tensor decomposition problem where the indices (i, j, or k) of some observed elements are partially missing.Towards the problem, we propose a probabilistic tensor decomposition model that handles missing indices as latent variables.To infer them, we derive an algorithm based on stochastic variational inference, which enables to leverage the information from the incomplete data scalably.
446	When Does Label Propagation Fail? A View from a Network Generative Model	Yuto Yamaguchi, Kohei Hayashi	In this paper, we answer the above questions by interpreting LP from a statistical viewpoint.
447	FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services	Toshihiko Yamasaki, Jiani Hu, Shumpei Sano, Kiyoharu Aizawa	In this study, we address two emerging yet challenging problems in social media: (1) scoring the text tags in terms of the influence to the numbers of views, comments, and favorite ratings of images and videos on content sharing services, and (2) recommending additional tags to increase such popularity-related numbers.
448	Semi-Supervised Deep Hashing with a Bipartite Graph	Xinyu Yan, Lijun Zhang, Wu-Jun Li	In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipartite Graph (DHBG), to simultaneously learn embeddings, features and hash codes.
449	Predicting Human Interaction via Relative Attention Model	Yichao Yan, Bingbing Ni, Xiaokang Yang	In this work, we propose a relative attention model to explicitly address these difficulties.
450	Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation	Yuguang Yan, Wen Li, Michael Ng, Mingkui Tan, Hanrui Wu, Huaqing Min, Qingyao Wu	In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data.
451	Life-Stage Modeling by Customer-Manifold Embedding	Jing-Wen Yang, Yang Yu, Xiao-Peng Zhang	In this paper, we propose to discover a latent space, called customer-manifold, on which a position corresponds to a customer stage.
452	Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network	Jufeng Yang, Dongyu She, Ming Sun	In this work, we address the problem via label distribution learning (LDL) and develop a multi-task deep framework by jointly optimizing both classification and distribution prediction.
453	Positive unlabeled learning via wrapper-based adaptive sampling	Pengyi Yang, Wei Liu, Jean Yang	In this study, we propose an adaptive sampling (AdaSampling) approach that utilises prediction probabilities from a model to iteratively update the training data.
454	Learning to Read Irregular Text with Attention Mechanisms	Xiao Yang, Dafang He, Zihan Zhou, Daniel Kifer, C. Lee Giles	We present a robust end-to-end neural-based model to attentively recognize text in natural images.
455	Modal Consistency based Pre-Trained Multi-Model Reuse	Yang Yang, De-Chuan Zhan, Xiang-Yu Guo, Yuan Jiang	In this paper, aiming at the lack of evaluation on reliability, the potential consistency spread on different modalities is utilized.
456	A Robust Noise Resistant Algorithm for POI Identification from Flickr Data	Yiyang Yang, Zhiguo Gong, Qing Li, Leong Hou U, Ruichu Cai, Zhifeng Hao	To solve the problem, we propose a technique based on the local drastic changes of the data density.
457	Multi-Task Deep Reinforcement Learning for Continuous Action Control	Zhaoyang Yang, Kathryn Merrick, Hussein Abbass, Lianwen Jin	In this paper, we propose a deep reinforcement learning algorithm to learn multiple tasks concurrently.
458	Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems	Quanming Yao, James T. Kwok, Fei Gao, Wei Chen, Tie-Yan Liu	In this paper, we propose an efficient accelerate proximal gradient (niAPG) algorithm for nonconvex problems.
459	Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps	Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang	Most existing distance metric learning methods obtain metric based on the raw features and side information but neglect the reliability of them.
460	A Deep Neural Network for Chinese Zero Pronoun Resolution	Qingyu Yin, Weinan Zhang, Yu Zhang, Ting Liu	To address these weaknesses, we propose a novel zero pronoun-specific neural network, which is capable of representing zero pronouns by utilizing the contextual information at the semantic level.
461	Learning Co-Substructures by Kernel Dependence Maximization	Sho Yokoi, Daichi Mochihashi, Ryo Takahashi, Naoaki Okazaki, Kentaro Inui	We, therefore, propose the novel machine learning task of extracting a strongly associated substructure pair (co-substructure) from each input item pair.
462	Privileged Multi-label Learning	Shan You, Chang Xu, Yunhe Wang, Chao Xu, Dacheng Tao	This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems.
463	Link Prediction with Spatial and Temporal Consistency in Dynamic Networks	Wenchao Yu, Wei Cheng, Charu C Aggarwal, Haifeng Chen, Wei Wang	To address this limitation, in this paper, we propose a link prediction model with spatial and temporal consistency (LIST), to predict links in a sequence of networks over time.
464	Single-Pass PCA of Large High-Dimensional Data	Wenjian Yu, Yu Gu, Jian Li, Shenghua Liu, Yaohang Li	In this work, a single-pass randomized algorithm is proposed to compute PCA with only one pass over the data.
465	Open Category Classification by Adversarial Sample Generation	Yang Yu, Wei-Yang Qu, Nan Li, Zimin Guo	In this paper, adopting the idea of adversarial learning, we propose the ASG framework for open-category classification.
466	Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization	Yue Yu, Longbo Huang	We propose the first ADMM based algorithm named com SVR ADMM, and show that com SVR ADMM converges linearly for strongly convex and Lipschitz smooth objectives, and has a convergence rate of $O(\logS/S)$, which improves upon the $O(S^{-4/9})$ rate in \cite{wang2016accelerating} when the objective is convex and Lipschitz smooth.
467	Deep-dense Conditional Random Fields for Object Co-segmentation	Zehuan Yuan, Tong Lu, Yirui Wu	We introduce a deep-dense conditional random field framework to infer co-occurrence maps.
468	User Profile Preserving Social Network Embedding	Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang	To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network.
469	A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning	Honglun Zhang, Liqiang Xiao, Yongkun Wang, Yaohui Jin	In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks.
470	ME-MD: An Effective Framework for Neural Machine Translation with Multiple Encoders and Decoders	Jinchao Zhang, Qun Liu, Jie Zhou	To further enhance NMT, we propose to extend the original encoder-decoder framework to a novel one, which has multiple encoders and decoders (ME-MD).
471	Adaptive Manifold Regularized Matrix Factorization for Data Clustering	Lefei Zhang, Qian Zhang, Bo Du, Jane You, Dacheng Tao	In this paper, we propose to consider the observed data clustering as a robust matrix factorization point of view, and learn an affinity matrix simultaneously to regularize the proposed matrix factorization.
472	Adaptively Unified Semi-supervised Learning for Cross-Modal Retrieval	Liang Zhang, Bingpeng Ma, Jianfeng He, Guorong Li, Qingming Huang, Qi Tian	Motivated by the fact that both relevancy of class labels and unlabeled data can help to strengthen multi-modal correlation, this paper proposes a novel method for cross-modal retrieval.
473	DRLnet: Deep Difference Representation Learning Network and An Unsupervised Optimization Framework	Puzhao Zhang, Maoguo Gong, Hui Zhang, Jia Liu	In this paper, we propose a novel difference representation learning network (DRLnet) and an effective optimization framework without any supervision.
474	Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network	Qi Zhang, Jiawen Wang, Haoran Huang, Xuanjing Huang, Yeyun Gong	Motivated by the successful use of the attention mechanism, we propose a co-attention network incorporating textual and visual information to recommend hashtags for multimodal tweets.
475	Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions	Rui Zhang, Sheng Tang, Min Lin, Jintao Li, Shuicheng Yan	Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions
476	Robust Regression via Heuristic Hard Thresholding	Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu	This paper proposes a novel Robust Least squares regression algorithm via Heuristic Hard thresholding (RLHH), that concurrently addresses all the above challenges.
477	Multi-Instance Learning with Key Instance Shift	Ya-Lin Zhang, Zhi-Hua Zhou	In this paper, we address the problem that the distribution of key instances varies between training and test phase.
478	Multimodal Linear Discriminant Analysis via Structural Sparsity	Yu Zhang, Yuan Jiang	To solve this problem, we propose a method called multimodal linear discriminant analysis (MLDA).
479	Weighted Double Q-learning	Zongzhang Zhang, Zhiyuan Pan, Mykel J. Kochenderfer	This paper introduces a weighted double Q-learning algorithm, which is based on the construction of the weighted double estimator, with the goal of balancing between the overestimation in the single estimator and the underestimation in the double estimator.
480	Tensor Based Knowledge Transfer Across Skill Categories for Robot Control	Chenyang Zhao, Timothy M. Hospedales, Freek Stulp, Olivier Sigaud	In this paper we go significantly further and investigate generalisation across qualitatively different classes of control skills.
481	Learning Discriminative Recommendation Systems with Side Information	Feipeng Zhao, Yuhong Guo	In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems.
482	Random Shifting for CNN: a Solution to Reduce Information Loss in Down-Sampling Layers	Gangming Zhao, Jingdong Wang, Zhaoxiang Zhang	In this paper, we propose a novel random strategy to alleviate these problems by embedding random shifting in the down-sampling layers during the training process.
483	Hierarchical Feature Selection with Recursive Regularization	Hong Zhao, Pengfei Zhu, Ping Wang, Qinghua Hu	In this paper, we propose a new technique for hierarchical feature selection based on recursive regularization.
484	Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning	Shanshan Zhao, Xi Li, Omar El Farouk Bourahla	Motivated by this observation, we propose an end-to-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation.
485	ContextCare: Incorporating Contextual Information Networks to Representation Learning on Medical Forum Data	Stan Zhao, Meng Jiang, Quan Yuan, Bing Qin, Ting Liu, ChengXiang Zhai	In this paper, we propose a novel and general representation learning method ContextCare for human generated health-related data, which learns the latent relationship between symptoms and diseases from the symptom-disease diagnosis network for disease prediction, disease category prediction and disease clustering.
486	Deep Multiple Instance Hashing for Object-based Image Retrieval	Wanqing Zhao, Ziyu Guan, Hangzai Luo, Jinye Peng, Jianping Fan	In this work, we propose a weakly-supervised Deep Multiple Instance Hashing (DMIH) framework for object-based image retrieval.
487	TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets	Xin Zhao, Guiguang Ding, Yuchen Guo, Jungong Han, Yue Gao	Inspired by the Generative Adversarial Nets (GANs), this paper presents a new model that is able to Turn Cross-view Hashing into single-view hashing (TUCH), thus enabling the information of image to be preserved as much as possible.
488	Video Question Answering via Hierarchical Spatio-Temporal Attention Networks	Zhou Zhao, Qifan Yang, Deng Cai, Xiaofei He, Yueting Zhuang	In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoder-decoder learning framework.
489	Link Prediction via Ranking Metric Dual-Level Attention Network Learning	Zhou Zhao, Ben Gao, Vincent W. Zheng, Deng Cai, Xiaofei He, Yueting Zhuang	In this paper, we consider the problem of link prediction from the viewpoint of learning discriminative path-based proximity ranking metric embedding.
490	Microblog Sentiment Classiﬁcation via Recurrent Random Walk Network Learning	Zhou Zhao, Hanqing Lu, Deng Cai, Xiaofei He, Yueting Zhuang	In this paper, we consider the problem of microblog sentiment classiﬁcation from the viewpoint of heterogeneous MSC network embedding.
491	Tensor Completion with Side Information: A Riemannian Manifold Approach	Tengfei Zhou, Hui Qian, Zebang Shen, Chao Zhang, Congfu Xu	To fill the gap, in this paper, a novel Riemannian model is proposed to tightly integrate the original model and the side information by overcoming their inconsistency.
492	Binary Linear Compression for Multi-label Classification	Wen-Ji Zhou, Yang Yu, Min-Ling Zhang	In this paper, we disclose that mappings to a low-dimensional multi-label regression problem can be worse than mapping to a classification problem, since regression requires more complex model than classification.
493	Deep Forest: Towards An Alternative to Deep Neural Networks	Zhi-Hua Zhou, Ji Feng	In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks.
494	Diverse Neuron Type Selection for Convolutional Neural Networks	Guibo Zhu, Zhaoxiang Zhang, Xu-Yao Zhang, Cheng-Lin Liu	Inspired by neuroscience findings, we introduce and define two types of neurons with different activation functions for artificial neural networks: excitatory and inhibitory neurons, which can be adaptively selected by self-learning.
495	Locality Constrained Deep Supervised Hashing for Image Retrieval	Hao Zhu, Shenghua Gao	In this paper, we propose a novel Locality-Constrained Deep Supervised Hashing.
496	Deep Graphical Feature Learning for Face Sketch Synthesis	Mingrui Zhu, Nannan Wang, Xinbo Gao, Jie Li	We present a novel face sketch synthesis method combining generative exemplar-based method and discriminatively trained deep convolutional neural networks (dCNNs) via a deep graphical feature learning framework.
497	Adaptive Hypergraph Learning for Unsupervised Feature Selection	Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang, Rongyao Hu, Wei He	To address these issues, we propose a new UFS method to jointly learn the similarity matrix and conduct both subspace learning (via learning a dynamic hypergraph) and feature selection (via a sparsity constraint).
498	No Learner Left Behind: On the Complexity of Teaching Multiple Learners Simultaneously	Xiaojin Zhu, Ji Liu, Manuel Lopes	We present a theoretical study of algorithmic teaching in the setting where the teacher must use the same training set to teach multiple learners.
499	Dependency Exploitation: A Unified CNN-RNN Approach for Visual Emotion Recognition	Xinge Zhu, Liang Li, Weigang Zhang, Tianrong Rao, Min Xu, Qingming Huang, Dong Xu	In this paper, we propose a unified CNN-RNN model to predict the emotion based on the fused features from different levels by exploiting the dependency among them.
500	What to Do Next: Modeling User Behaviors by Time-LSTM	Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai	In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users’ sequential actions.
501	Object Recognition with and without Objects	Zhuotun Zhu, Lingxi Xie, Alan Yuille	In this paper, we train deep neural networks on the foreground (object) and background (context) regions of images respectively.
502	Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory	Chenyi Zhuang, Nicholas Jing Yuan, Ruihua Song, Xing Xie, Qiang Ma	In this paper, we propose a multi-view learning framework for presenting the construction of a new urban movement knowledge graph, which could greatly facilitate the research domains mentioned above.
503	Cake Cutting: Envy and Truth	Xiaohui Bei, Ning Chen, Guangda Huzhang, Biaoshuai Tao, Jiajun Wu	We study envy-free cake cutting with strategic agents, where each agent may manipulate his private information in order to receive a better allocation.
504	Networked Fairness in Cake Cutting	Xiaohui Bei, Youming Qiao, Shengyu Zhang	We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure.
505	Deep Multi-species Embedding	Di Chen, Yexiang Xue, Daniel Fink, Shuo Chen, Carla P. Gomes	We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network.
506	Opinion-aware Knowledge Graph for Political Ideology Detection	Wei Chen, Xiao Zhang, Tengjiao Wang, Bishan Yang, Yi Li	Specifically, we propose a novel approach to political ideology detection that makes predictions based on an opinion-aware knowledge graph.
507	Exploiting Music Play Sequence for Music Recommendation	Zhiyong Cheng, Jialie Shen, Lei Zhu, Mohan Kankanhalli, Liqiang Nie	In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems.
508	Social Pressure in Opinion Games	Diodato Ferraioli, Carmine Ventre	Motivated by privacy and security concerns in online social networks, we study the role of social pressure in opinion games.
509	Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex	Chao Gao, Martin Müller, Ryan Hayward	We describe FDFPN-CNN, a new focused DFPN search that uses convolutional neural networks.
510	DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning	Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim	In this paper, we propose an intelligent system called DeepAM for automatically mining API mappings from a large-scale code corpus without bilingual projects.
511	Playing Repeated Network Interdiction Games with Semi-Bandit Feedback	Qingyu Guo, Bo An, Long Tran-Thanh	In particular, we model the repeated network interdiction game with no prior knowledge as an online linear optimization problem, for which a novel and efficient online learning algorithm, SBGA, is proposed, which exploits the unique semi-bandit feedback in network security domains.
512	Comparing Strategic Secrecy and Stackelberg Commitment in Security Games	Qingyu Guo, Bo An, Branislav Bošanský, Christopher Kiekintveld	In this paper, we overcome these restrictions and analyze the tradeoff between strategic secrecy and commitment in security games.
513	Modeling Physicians’ Utterances to Explore Diagnostic Decision-making	Xuan Guo, Rui Li, Qi Yu, Anne Haake	In this paper, we contribute to the field by exploring diagnostic decision-making via modeling physicians’ utterances of medical concepts during image-based diagnoses.
514	Game Engine Learning from Video	Matthew Guzdial, Boyang Li, Mark O. Riedl	In this work we present a novel approach to learn a forward simulation model via simple search over pixel input.
515	Who to Invite Next? Predicting Invitees of Social Groups	Yu Han, Jie Tang	In this paper, we formalize a novel problem of predicting potential invitees of groups.
516	Fashion Style Generator	Shuhui Jiang, Yun Fu	In this paper, we focus on a new problem: applying artificial intelligence to automatically generate fashion style images.
517	Exploring Personalized Neural Conversational Models	Satwik Kottur, Xiaoyu Wang, Vitor Carvalho	Based on the tradeoffs of different models, we propose a new generative dialogue model conditioned on speakers as well as context history that outperforms all previous models on both retrieval and generative metrics.
518	Stratified Strategy Selection for Unit Control in Real-Time Strategy Games	Levi H. S. Lelis	In this paper we introduce Stratified Strategy Selection (SSS), a novel search algorithm for micromanaging units in real-time strategy (RTS) games.
519	Defending Against Man-In-The-Middle Attack in Repeated Games	Shuxin Li, Xiaohong Li, Jianye Hao, Bo An, Zhiyong Feng, Kangjie Chen, Chengwei Zhang	Given the impracticability of computing Nash equilibrium directly, we propose practical adaptive algorithms for the defenders and the attacker to learn towards the unique Nash equilibrium through repeated interactions.
520	How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source	Jiaqing Liang, Sheng Zhang, Yanghua Xiao	In this paper, we investigate how to keep the freshness of the knowledge base by synchronizing it with its data source (usually encyclopedia websites). To overcome the weakness, we propose a set of synchronization principles upon which we build an Update System for knowledge Base (USB) with an update frequency predictor of entities as the core component.
521	Tactics of Adversarial Attack on Deep Reinforcement Learning Agents	Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun	We propose a novel method to determine when an adversarial example should be crafted and applied.
522	Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training	Xingjun Ma, Sudanthi Wijewickrema, Shuo Zhou, Yun Zhou, Zakaria Mhammedi, Stephen O’Leary, James Bailey	It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT.
523	Staying Ahead of the Game: Adaptive Robust Optimization for Dynamic Allocation of Threat Screening Resources	Sara Marie Mc Carthy, Phebe Vayanos, Milind Tambe	We thus propose a novel framework for dynamic allocation of threat screening resources that explicitly accounts for uncertainty in the screenee arrival times.
524	Blue Skies: A Methodology for Data-Driven Clear Sky Modelling	Kartik Palani, Ramachandra Kota, Amar Prakash Azad, Vijay Arya	In this paper, we present a data-driven methodology, Blue Skies, for modelling clear sky irradiance solely based on historical irradiance measurements.
525	Thwarting Vote Buying Through Decoy Ballots	David C. Parkes, Paul Tylkin, Lirong Xia	We show that an Election Authority can significantly reduce the power of vote buying through a small number of optimally distributed decoys, and model societal processes by which decoys could be distributed.
526	Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach	Lahari Poddar, Wynne Hsu, Mong Li Lee	We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user’s aspect bias and (ii) latent intrinsic quality of an item.
527	Unified Representation and Lifted Sampling for Generative Models of Social Networks	Pablo Robles-Granda, Sebastian Moreno, Jennifer Neville	In this paper, we identify a family of GNMs that share a common latent structure and create a Bayesian network (BN) representation that captures their common form.
528	Cognitive-Inspired Conversational-Strategy Reasoner for Socially-Aware Agents	Oscar J. Romero, Ran Zhao, Justine Cassell	In this work we propose a novel module for a dialogue system that allows a conversational agent to utter phrases that do not just meet the system’s task intentions, but also work towards achieving the system’s social intentions.
529	When Security Games Hit Traffic: Optimal Traffic Enforcement Under One Sided Uncertainty	Ariel Rosenfeld, Sarit Kraus	In this paper, we present a novel model and an optimizing algorithm for mitigating some of the computational challenges of real-world traffic enforcement allocation in large road networks.
530	Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects	Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus	In this paper, we propose and evaluate a novel method, based on human psychology literature, which we show to be both effective and efficient, for both expert and non-expert designers, in injecting human knowledge for speeding up tabular RL.
531	A Monte Carlo Tree Search approach to Active Malware Analysis	Riccardo Sartea, Alessandro Farinelli	To devise such actions, we model AMA as a stochastic game between an analyzer agent and a malware sample, and we propose a reinforcement learning algorithm based on Monte Carlo Tree Search.
532	Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution	Guangyao Shen, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, Wenwu Zhu	Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media.
533	The Minds of Many: Opponent Modeling in a Stochastic Game	Friedrich Burkhard von der Osten, Michael Kirley, Tim Miller	In this paper, we extend a recently introduced technique for opponent modeling based on Theory of Mind reasoning.
534	Interactive Narrative Personalization with Deep Reinforcement Learning	Pengcheng Wang, Jonathan Rowe, Wookhee Min, Bradford Mott, James Lester	In this paper we present a deep RL-based interactive narrative generation framework that leverages synthetic data produced by a bipartite simulated player model.
535	Predicting the Quality of Short Narratives from Social Media	Tong Wang, Ping Chen, Boyang Li	To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research.
536	A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing	Qikun Xiang, Jie Zhang, Ido Nevat, Pengfei Zhang	We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field.
537	Online Reputation Fraud Campaign Detection in User Ratings	Chang Xu, Jie Zhang, Zhu Sun	One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories.However, these campaigns are constantly evolving and changing tactics to evade detection.For example, they can launch early attacks on the items to quickly dominate the reputations.They can also whitewash themselves through creating new accounts for subsequent attacks.It is thus challenging for existing approaches working on historical data to promptly react to such emerging fraud activities.In this paper, we conduct RFC detection in online fashion, so as to spot campaign activities as early as possible.This leads to a unified and scalable optimization framework, FraudScan, that can adapt to emerging fraud patterns over time.Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework.
538	Predicting Alzheimer’s Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model	Jie Xu, Cheng Deng, Xinbo Gao, Dinggang Shen, Heng Huang	In this paper, we propose a robust low-rank structured sparse regression method (RLSR) to address this issue.
539	Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN	Yanyu Xu, Nianyi Li, Junru Wu, Jingyi Yu, Shenghua Gao	We model PSM based on universal saliency map (USM) shared by different participants and adopt a multi-task CNN framework to estimate the discrepancy between PSM and USM.
540	Fast Network Embedding Enhancement via High Order Proximity Approximation	Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu	We propose Network Embedding Update (NEU) algorithm which implicitly approximates higher order proximities with theoretical approximation bound and can be applied on any NRL methods to enhance their performances.
541	A Convolutional Approach for Misinformation Identification	Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan	In this paper, we propose a novel method, Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN).
542	No Time to Observe: Adaptive Influence Maximization with Partial Feedback	Jing Yuan, Shaojie Tang	To fill the gap between these two models, we propose partial-feedback model, which allows us to select a seed at any intermediate stage.
543	Socialized Word Embeddings	Ziqian Zeng, Yichun Yin, Yangqiu Song, Ming Zhang	In this paper, we propose a socialized word embedding algorithm which can consider both user’s personal characteristics of language use and the user’s social relationship on social media.
544	Efficient Private ERM for Smooth Objectives	Jiaqi Zhang, Kai Zheng, Wenlong Mou, Liwei Wang	In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms.
545	A Causal Framework for Discovering and Removing Direct and Indirect Discrimination	Lu Zhang, Yongkai Wu, Xintao Wu	In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data is used for predictive analysis (e.g., building classifiers).
546	Optimal Escape Interdiction on Transportation Networks	Youzhi Zhang, Bo An, Long Tran-Thanh, Zhen Wang, Jiarui Gan, Nicholas R. Jennings	Therefore, in this paper, we study the problem of efficiently scheduling security resources for interdicting the escaping attacker.
547	Efficient Label Contamination Attacks Against Black-Box Learning Models	Mengchen Zhao, Bo An, Wei Gao, Teng Zhang	In this paper, we develop a Projected Gradient Ascent (PGA) algorithm to compute LCAs on a family of empirical risk minimizations and show that an attack on one victim model can also be effective on other victim models.
548	A Group-Based Personalized Model for Image Privacy Classification and Labeling	Haoti Zhong, Anna Squicciarini, David Miller, Cornelia Caragea	We propose a Group-Based Personalized Model for image privacy classification in online social media sites, which learns a set of archetypical privacy models (groups), and associates a given user with one of these groups.
549	A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging	Xinchi Chen, Xipeng Qiu, Xuanjing Huang	In this work, we propose a feature-enriched neural model for joint Chinese word segmentation and part-of-speech tagging task.
550	Multimodal Storytelling via Generative Adversarial Imitation Learning	Zhiqian Chen, Xuchao Zhang, Arnold P. Boedihardjo, Jing Dai, Chang-Tien Lu	This paper proposes a method, multimodal imitation learning via Generative Adversarial Networks(MIL-GAN), to directly model users’ interests as reflected by various data.
551	Joint Training for Pivot-based Neural Machine Translation	Yong Cheng, Qian Yang, Yang Liu, Maosong Sun, Wei Xu	In this work, we introduce a joint training algorithm for pivot-based neural machine translation.
552	Solving Probability Problems in Natural Language	Anton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, Luc de Raedt	In this paper, we develop a two-step end-to-end fully automated approach for solving such questions that is able to automatically provide answers to exercises about probability formulated in natural language.In the first step, a question formulated in natural language is analysed and transformed into a high-level model specified in a declarative language.
553	Stance Classification with Target-specific Neural Attention	Jiachen Du, Ruifeng Xu, Yulan He, Lin Gui	To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism.
554	An Attention-based Regression Model for Grounding Textual Phrases in Images	Ko Endo, Masaki Aono, Eric Nichols, Kotaro Funakoshi	In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction.
555	Effective Deep Memory Networks for Distant Supervised Relation Extraction	Xiaocheng Feng, Jiang Guo, Bing Qin, Ting Liu, Yongjie Liu	Typically it can be formalized as a multi-instance multi-label problem.In this paper, we introduce a novel neural approach for distant supervised (RE) with specific focus on attention mechanisms.Unlike the feature-based logistic regression model and compositional neural models such as CNN, our approach includes two major attention-based memory components, which is capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair, as well as the intrinsic dependencies between relations.Such importance degree and dependency relationship are calculated with multiple computational layers, each of which is a neural attention model over an external memory.
556	Understanding and Exploiting Language Diversity	Fausto Giunchiglia, Khuyagbaatar Batsuren, Gabor Bella	The main goal of this paper is to describe a general approach to the problem of understanding linguistic phenomena, as they appear in lexical semantics, through the analysis of large scale resources, while exploiting these results to improve the quality of the resources themselves.
557	Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks	Jizhou Huang, Wei Zhang, Shiqi Zhao, Shiqiang Ding, Haifeng Wang	In this paper, we propose an effective pairwise ranking model by leveraging clickthrough data of a Web search engine to address these two problems. We first construct large-scale training data by leveraging the query-title pairs derived from clickthrough data of a Web search engine.
558	SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition	Wenqiang Lei, Xuancong Wang, Meichun Liu, Ilija Ilievski, Xiangnan He, Min-Yen Kan	By saving an order of magnitude in parameters, our proposed model achieves equivalent performance, but trains seven times faster.
559	MAT: A Multimodal Attentive Translator for Image Captioning	Chang Liu, Fuchun Sun, Changhu Wang, Feng Wang, Alan Yuille	In this work we formulate the problem of image captioning as a multimodal translation task.
560	How Unlabeled Web Videos Help Complex Event Detection?	Huan Liu, Qinghua Zheng, Minnan Luo, Dingwen Zhang, Xiaojun Chang, Cheng Deng	In this paper, we propose a new robust dictionary learning framework for complex event detection, which is able to handle both labeled and easy-to-get unlabeled web videos by sharing the same dictionary.
561	A Structural Representation Learning for Multi-relational Networks	Lin Liu, Xin Li, William K. Cheung, Chengcheng Xu	With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations.
562	Dynamic Compositional Neural Networks over Tree Structure	Pengfei Liu, Xipeng Qiu, Xuanjing Huang	In spite of their success, most existing models suffer from the underfitting problem: they recursively use the same shared compositional function throughout the whole compositional process and lack expressive power due to inability to capture the richness of compositionality.In this paper, we address this issue by introducing the dynamic compositional neural networks over tree structure (DC-TreeNN), in which the compositional function is dynamically generated by a meta network.The role of meta-network is to capture the metaknowledge across the different compositional rules and formulate them.
563	Adaptive Semantic Compositionality for Sentence Modelling	Pengfei Liu, Xipeng Qiu, Xuanjing Huang	To address this problem, we introduce a parameterized compositional switch, which outputs a scalar to adaptively determine whether the meaning of a phrase should be composed of its two constituents.We evaluate our model on five datasets of sentiment classification and demonstrate its efficacy with qualitative and quantitative experimental analysis .
564	Interactive Attention Networks for Aspect-Level Sentiment Classification	Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang	In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.
565	Inverted Bilingual Topic Models for Lexicon Extraction from Non-parallel Data	Tengfei Ma, Tetsuya Nasukawa	In this paper, we try to address two challenges of applying topic models to lexicon extraction in non-parallel data: 1) hard to model the word relationship and 2) noisy seed dictionary.
566	Why Can’t You Convince Me? Modeling Weaknesses in Unpersuasive Arguments	Isaac Persing, Vincent Ng	Recent work on argument persuasiveness has focused on determining how persuasive an argument is. Motivated by this practical concern, we (1) annotate a corpus of debate comments with not only their persuasiveness scores but also the errors they contain, (2) propose an approach to persuasiveness scoring and error identification that outperforms competing baselines, and (3) show that the persuasiveness scores computed by our approach can indeed be explained by the errors it identifies.
567	Parsing Natural Language Conversations using Contextual Cues	Shashank Srivastava, Amos Azaria, Tom Mitchell	In this work, we focus on semantic parsing of natural language conversations. We create a dataset for semantic parsing of conversations, consisting of 113 real-life sequences of interactions of human users with an automated email assistant.
568	Automatic Assessment of Absolute Sentence Complexity	Sanja Stajner, Simone Paolo Ponzetto, Heiner Stuckenschmidt	Automatic Assessment of Absolute Sentence Complexity
569	Finding Prototypes of Answers for Improving Answer Sentence Selection	Wai Lok Tam, Namgi Han, Juan Ignacio Navarro-Horñiacek, Yusuke Miyao	In contrast, the present paper describes a simple technique to take advantage of such general-purpose tools for dealing with questions and answer sentences without changing the base system.
570	From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach	Jiwei Tan, Xiaojun Wan, Jianguo Xiao	In this paper, we investigate the extension of sentence summarization models to the document headline generation task.
571	Multi-Modal Word Synset Induction	Jesse Thomason, Raymond J. Mooney	Given pairs of images and text with noun phrase labels, we perform synset induction to produce collections of underlying concepts described by one or more noun phrases.
572	Conditional Generative Adversarial Networks for Commonsense Machine Comprehension	Bingning Wang, Kang Liu, Jun Zhao	We proposed a Conditional GANs in which the generator is conditioned by the context.
573	Joint Learning on Relevant User Attributes in Micro-blog	Jingjing Wang, Shoushan Li, Guodong Zhou	In this paper, we confront a novel scenario in user attribute classification where relevant user attributes are jointly learned, attempting to make the relevant attribute classification tasks help each other.
574	Learning Sentence Representation with Guidance of Human Attention	Shaonan Wang, Jiajun Zhang, Chengqing Zong	To that end, we propose two novel attention models, in which the attention weights are derived using significant predictors of human reading time, i.e., Surprisal, POS tags and CCG supertags.
575	Bilateral Multi-Perspective Matching for Natural Language Sentences	Zhiguo Wang, Wael Hamza, Radu Florian	In this work, we propose a bilateral multi-perspective matching (BiMPM) model.
576	DDoS Event Forecasting using Twitter Data	Zhongqing Wang, Yue Zhang	We propose a fine-grained hierarchical stream model to capture semantic information over infinitely long history, and reveal burstiness and trends.
577	A Neural Model for Joint Event Detection and Summarization	Zhongqing Wang, Yue Zhang	In this paper, we build a joint model to filter, cluster, and summarize the tweets for new events.
578	A Variational Autoencoding Approach for Inducing Cross-lingual Word Embeddings	Liangchen Wei, Zhi-Hong Deng	We propose a variational autoencoding approach for training bilingual word embeddings.
579	Learning to Identify Ambiguous and Misleading News Headlines	Wei Wei, Xiaojun Wan	In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately.
580	Improved Neural Machine Translation with Source Syntax	Shuangzhi Wu, Ming Zhou, Dongdong Zhang	In this paper we propose a simple but effective method to incorporate source-side long distance dependencies into NMT.
581	Symbolic Priors for RNN-based Semantic Parsing	Chunyang Xiao, Marc Dymetman, Claire Gardent	To alleviate this problem, we propose to exploit various sources of prior knowledge: the well-formedness of the logical forms is modeled by a weighted context-free grammar; the likelihood that certain entities present in the input utterance are also present in the logical form is modeled by weighted finite-state automata.
582	Fast Parallel Training of Neural Language Models	Tong Xiao, Jingbo Zhu, Tongran Liu, Chunliang Zhang	In this paper we present a sampling-based approach to reducing data transmission for better scaling of NLMs.
583	Lexical Sememe Prediction via Word Embeddings and Matrix Factorization	Ruobing Xie, Xingchi Yuan, Zhiyuan Liu, Maosong Sun	In this paper, we for the first time explore to automatically predict lexical sememes based on semantic meanings of words encoded by word embeddings.
584	A Correlated Topic Model Using Word Embeddings	Guangxu Xun, Yaliang Li, Wayne Xin Zhao, Jing Gao, Aidong Zhang	In this paper, we propose a novel correlated topic model using word embeddings.
585	Learning Conversational Systems that Interleave Task and Non-Task Content	Zhou Yu, Alexander Rudnicky, Alan Black	We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content.To test the effectiveness of the proposed framework, we developed a movie promotion dialog system.
586	AGRA: An Analysis-Generation-Ranking Framework for Automatic Abbreviation from Paper Titles	Jianbing Zhang, Yixin Sun, Shujian Huang, Cam-Tu Nguyen, Xiaoliang Wang, Xinyu Dai, Jiajun Chen, Yang Yu	In this paper, we propose to view the naming task as an artificial intelligence problem and create a data set in the domain of academic naming.
587	Segmenting Chinese Microtext: Joint Informal-Word Detection and Segmentation with Neural Networks	Meishan Zhang, Guohong Fu, Nan Yu	State-of-the-art Chinese word segmentation systems typically exploit supervised modelstrained on a standard manually-annotated corpus,achieving performances over 95% on a similar standard testing corpus.However, the performances may drop significantly when the same models are applied onto Chinese microtext.One major challenge is the issue of informal words in the microtext.Previous studies show that informal word detection can be helpful for microtext processing.In this work, we investigate it under the neural setting, by proposing a joint segmentation model that integrates the detection of informal words simultaneously.In addition, we generate training corpus for the joint model by using existing corpus automatically.Experimental results show that the proposed model is highly effective for segmentation of Chinese microtext.
588	Automatic Generation of Grounded Visual Questions	Shijie Zhang, Lizhen Qu, Shaodi You, Zhenglu Yang, Jiawan Zhang	In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image.
589	Entity Suggestion with Conceptual Expanation	Yi Zhang, Yanghua Xiao, Seung-won Hwang, Haixun Wang, X. Sean Wang, Wei Wang	In this paper, we propose two probabilistic entity suggestion models and their computation solutions.
590	Maximum Expected Likelihood Estimation for Zero-resource Neural Machine Translation	Hao Zheng, Yong Cheng, Yang Liu	To deal with this problem, we propose an approach to zero-resource NMT via maximum expected likelihood estimation.
591	Iterative Entity Alignment via Joint Knowledge Embeddings	Hao Zhu, Ruobing Xie, Zhiyuan Liu, Maosong Sun	In this paper, we present a novel approach for entity alignment via joint knowledge embeddings.
592	Efficient Optimal Search under Expensive Edge Cost Computation	Masataro Asai, Akihiro Kishimoto, Adi Botea, Radu Marinescu, Elizabeth M. Daly, Spyros Kotoulas	We present DEA*, an algorithm for problems with expensive edge cost computations.
593	Reduction Techniques for Model Checking and Learning in MDPs	Suda Bharadwaj, Stephane Le Roux, Guillermo Perez, Ufuk Topcu	We provide reduction techniques that al- low us to remove irrelevant transition probabilities: transition probabilities (known, or to be learned) that do not influence the maximal reachability probability.
594	Faster Conflict Generation for Dynamic Controllability	Nikhil Bhargava, Tiago Vaquero, Brian Williams	In this paper, we focus on speeding up the temporal plan relaxation problem for dynamically controllable systems.
595	Additive Merge-and-Shrink Heuristics for Diverse Action Costs	Gaojian Fan, Martin Müller, Robert Holte	In this paper, we demonstrate there are negative impacts of action cost diversity on merge-and-shrink (M&S), a successful abstraction method for producing high-quality heuristics for planning problems.
596	Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators	Guillem Francès, Miquel Ramírez, Nir Lipovetzky, Hector Geffner	The question we address in this paper is: can a planner that has access to the structure of states and goals only, approach the performance of planners that also have access to the structure of actions expressed in PDDL?
597	On Creating Complementary Pattern Databases	Santiago Franco, Álvaro Torralba, Levi H. S. Lelis, Mike Barley	In this paper we introduce a method that sequentially creates multiple PDBs which are later combined into a single heuristic function.
598	Beyond Forks: Finding and Ranking Star Factorings for Decoupled Search	Daniel Gnad, Valerie Poser, Jörg Hoffmann	Here, we introduce factoring strategies able to detect general star topologies, thereby extending the reach of decoupled search to new factorings and to new domains, sometimes resulting in significant performance improvements.
599	Intelligent Belief State Sampling for Conformant Planning	Alban Grastien, Enrico Scala	We propose a new method for conformant planning based on two ideas.
600	Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion	Hsu-Chieh Hu, Stephen F. Smith	In this paper, we present a hybrid approach of those two methods that incorporates the stability of queuing theory into the schedule-driven control.
601	Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control	Chao Huang, Xin Chen, Yifan Zhang, Shengchao Qin, Yifeng Zeng, Xuandong Li	In this paper, we propose a novel HMPC based method to address the navigation problem of multiple robots with switched linear dynamics.
602	Numeric Planning via Abstraction and Policy Guided Search	León Illanes, Sheila A. McIlraith	We describe a family of planning algorithms that takes a numeric planning problem and produces an abstracted representation that can be solved using any classical planner.
603	Factorized Asymptotic Bayesian Policy Search for POMDPs	Masaaki Imaizumi, Ryohei Fujimaki	This paper proposes a novel direct policy search (DPS) method with model selection for partially observed Markov decision processes (POMDPs).
604	Equi-Reward Utility Maximizing Design in Stochastic Environments	Sarah Keren, Luis Pineda, Avigdor Gal, Erez Karpas, Shlomo Zilberstein	To find an optimal modification sequence we present two novel solution techniques: a compilation that embeds design into a planning problem, allowing use of off-the-shelf solvers to find a solution, and a heuristic search in the modifications space, for which we present an admissible heuristic.
605	Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning	Dongcai Lu, Yi Zhou, Feng Wu, Zhao Zhang, Xiaoping Chen	In this paper, we propose a novel integrated task planning system for service robot in domestic domains.
606	Deceptive Path-Planning	Peta Masters, Sebastian Sardina	Building on recent developments in probabilistic goal-recognition, we propose a formula to calculate an optimal LDP and present strategies for the generation of deceptive paths by both simulation (‘showing the false’) and dissimulation (‘hiding the real’).
607	Lossy Compression of Pattern Databases Using Acyclic Random Hypergraphs	Mehdi Sadeqi, Howard J. Hamilton	In this paper, we introduce Acyclic Random Hypergraph Compression (ARHC), a domain-independent approach to compressing PDBs using acyclic random r-partite r-uniform hypergraphs.
608	Landmarks for Numeric Planning Problems	Enrico Scala, Patrik Haslum, Daniele Magazzeni, Sylvie Thiébaux	The paper proposes a relaxation-based method for their automated extraction directly from the problem structure, and shows how to exploit them to infer what we call disjunctive and additive hybrid action landmarks.
609	Generating Context-Free Grammars using Classical Planning	Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson	This paper presents a novel approach for generating Context-Free Grammars (CFGs) from small sets of input strings (a single input string in some cases).
610	Search and Learn: On Dead-End Detectors, the Traps they Set, and Trap Learning	Marcel Steinmetz, Jörg Hoffmann	Here, we create new synergy across these ideas.
611	Efficient, Safe, and Probably Approximately Complete Learning of Action Models	Roni Stern, Brendan Juba	In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent’s actions is given.
612	An Improved Approximation Algorithm for the Subpath Planning Problem and Its Generalization	Hanna Sumita, Yuma Yonebayashi, Naonori Kakimura, Ken-ichi Kawarabayashi	We propose a 3-approximation algorithm for SGPP.
613	Robust Advertisement Allocation	Shaojie Tang	Our main aim is to develop robust ad allocation algorithms, which can provide satisfactory performance across a spectrum of parameter settings, compared to the (parameter-specific) optimum solutions.
614	From Qualitative to Quantitative Dominance Pruning for Optimal Planning	Álvaro Torralba	We introduce a novel action selection pruning that uses this to prune any other successor.
615	Mechanism Design for Strategic Project Scheduling	Pradeep Varakantham, Na Fu	In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem).
616	Temporal Planning for Compilation of Quantum Approximate Optimization Circuits	Davide Venturelli, Minh Do, Eleanor Rieffel, Jeremy Frank	We investigate the application of temporal planners to the problem of compiling quantum circuits to emerging quantum hardware.
617	Heuristic Online Goal Recognition in Continuous Domains	Mor Vered, Gal A. Kaminka	In this paper, we utilize a different PRP formulation which allows for online goal recognition, and for application in continuous spaces.
618	New Metrics and Algorithms for Stochastic Goal Recognition Design Problems	Christabel Wayllace, Ping Hou, William Yeoh	In this paper, we make the following contributions: (1) We propose a new wcd metric, called all-goals wcd (wcdag), that remedies this inconsistency; (2) We introduce a new metric, called expected-case distinctiveness (ecd), that weighs the possible goals based on their importance; (3) We provide theoretical results comparing these different metrics as well as the complexity of computing them optimally; and (4) We describe new efficient algorithms to compute the wcdag and ecd values.
619	Hierarchical Task Network Planning with Task Insertion and State Constraints	Zhanhao Xiao, Andreas Herzig, Laurent Perrussel, Hai Wan, Xiaoheng Su	We extend hierarchical task network planning with task insertion (TIHTN) by introducing state constraints, called TIHTNS.
620	A Scalable Approach to Chasing Multiple Moving Targets with Multiple Agents	Fan Xie, Adi Botea, Akihiro Kishimoto	We introduce a sub-optimal but scalable approach that assigns individual agents to individual targets and that can dynamically re-compute such assignments.
621	Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction	Kai Xu, Kaiming Xiao, Quanjun Yin, Yabing Zha, Cheng Zhu	In this work, we propose a Markov Decision Process-based goal recognition approach tailored to a dynamic shortest-path local network interdiction (DSPLNI) problem.
622	Dual Track Multimodal Automatic Learning through Human-Robot Interaction	Shuqiang Jiang, Weiqing Min, Xue Li, Huayang Wang, Jian Sun, Jiaqi Zhou	To solve this problem, we propose a Dual Track Multimodal Automatic Learning (DTMAL) system, which consists of two components: Hybrid Incremental Learning (HIL) from the vision track and Multimodal Knowledge Extraction (MKE) from the knowledge track.
623	Locality Preserving Matching	Jiayi Ma, Ji Zhao, Hanqi Guo, Junjun Jiang, Huabing Zhou, Yuan Gao	We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities.
624	Salient Object Detection with Semantic Priors	Tam V. Nguyen, Luoqi Liu	In this paper, we propose integrating semantic priors into the salient object detection process.
625	Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context	Rohan Paul, Andrei Barbu, Sue Felshin, Boris Katz, Nicholas Roy	We present an approach to grounding natural language utterances in the context of factual information gathered through natural-language interactions and past visual observations.
626	Maintaining Communication in Multi-Robot Tree Coverage	Mor Sinay, Noa Agmon, Oleg Maksimov, Sarit Kraus, David Peleg	In this paper we study the problem of multi-robot coverage, in which the robots must obey a strong communication restriction: they should maintain connectivity between teammates throughout the coverage.
627	Combining Models from Multiple Sources for RGB-D Scene Recognition	Xinhang Song, Shuqiang Jiang, Luis Herranz	In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations.
628	Fast Preprocessing for Robust Face Sketch Synthesis	Yibing Song, Jiawei Zhang, Linchao Bao, Qingxiong Yang	In this paper, we propose a fast preprocessing method named Bidirectional Luminance Remapping (BLR), which interactively adjust the lighting of training and input photos.
629	Learning to Hallucinate Face Images via Component Generation and Enhancement	Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang	We propose a two-stage method for face hallucination.
630	Cross-Granularity Graph Inference for Semantic Video Object Segmentation	Huiling Wang, Tinghuai Wang, Ke Chen, Joni-Kristian Kämäräinen	In order to acquire high-quality tracklets, we propose a transductive inference model which is capable of calibrating short-range noisy object tracklets with respect to long-range dependencies and high-level context cues.
631	Is My Object in This Video? Reconstruction-based Object Search in Videos	Tan Yu, Jingjing Meng, Junsong Yuan	To alleviate the computational and memory cost, we propose the Reconstruction-based Object SEarch (ROSE) method.It characterizes a huge corpus of features of possible spatial-temporal locations in the video into the parameters of the reconstruction model.
632	Image Gradient-based Joint Direct Visual Odometry for Stereo Camera	Jianke Zhu	To tackle this critical problem, we propose a novel scheme for stereo odometry in this paper, which is able to improve the convergence with more accurate pose.
633	Adaptive Elicitation of Preferences under Uncertainty in Sequential Decision Making Problems	Nawal Benabbou, Patrice Perny	This paper aims to introduce an adaptive preference elicitation method for interactive decision support in sequential decision problems.
634	COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs	Madison Clark-Turner, Christopher Amato	To that end, we present a framework for representing and generating Dec-POMDP policies that explicitly include continuous observations.
635	Fair and Efficient Social Choice in Dynamic Settings	Rupert Freeman, Seyed Majid Zahedi, Vincent Conitzer	We present and analyze two greedy algorithms for this problem, including the classic Proportional Fair (PF) algorithm.
636	Incremental Decision Making Under Risk with the Weighted Expected Utility Model	Hugo Gilbert, Nawal Benabbou, Patrice Perny, Olivier Spanjaard, Paolo Viappiani	We propose here a new incremental elicitation procedure to progressively reduce the imprecision about these functions until a robust decision can be made.
637	Coarse-to-Fine Lifted MAP Inference in Computer Vision	Haroun Habeeb, Ankit Anand, Mausam, Parag Singla	We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs.
638	Variational Mixtures of Gaussian Processes for Classification	Chen Luo, Shiliang Sun	In this work, we propose a new Mixture of Gaussian Processes for Classification (MGPC).
639	Weighted Model Integration with Orthogonal Transformations	David Merrell, Aws Albarghouthi, Loris D’Antoni	We demonstrate the challenges of hyperrectangular decomposition and present a novel technique that utilizes orthogonal transformations to transform formulas in order to enable efficient inference.
640	Efficient Inference for Untied MLNs	Somdeb Sarkhel, Deepak Venugopal, Nicholas Ruozzi, Vibhav Gogate	When the treewidth is large, we propose an over-symmetric approximation and experimentally demonstrate that it is both fast and accurate.
641	Order Statistics for Probabilistic Graphical Models	David Smith, Sara Rouhani, Vibhav Gogate	We consider the problem of computing r-th order statistics, namely finding an assignment having rank r in a probabilistic graphical model.
642	Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data	Ruohui Wang, Dahua Lin	To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes.
643	XOR-Sampling for Network Design with Correlated Stochastic Events	Xiaojian Wu, Yexiang Xue, Bart Selman, Carla P. Gomes	In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated.
644	Robust Quadratic Programming for Price Optimization	Akihiro Yabe, Shinji Ito, Ryohei Fujimaki	For this particular uncertainty, we propose novel robust quadratic programming algorithms for conservative lower-bound maximization.
645	Single-Image 3D Scene Parsing Using Geometric Commonsense	Chengcheng Yu, Xiaobai Liu, Song-Chun Zhu	This paper presents a unified grammatical framework capable of reconstructing a variety of scene types (e.g., urban, campus, county etc.) from a single input image.
646	Dynamic Programming Bipartite Belief Propagation For Hyper Graph Matching	Zhen Zhang, Julian McAuley, Yong Li, Wei Wei, Yanning Zhang, Qinfeng Shi	In this paper, we formulate hyper graph matching problems as constrained MAP inference problems in graphical models.
647	Approximating Discrete Probability Distribution of Image Emotions by Multi-Modal Features Fusion	Sicheng Zhao, Guiguang Ding, Yue Gao, Jungong Han	In this paper, we propose a novel machine learning approach that formulates the categorical image emotions as a discrete probability distribution (DPD).
648	Plato’s Cave in the Dempster-Shafer land–the Link between Pignistic and Plausibility Transformations	Chunlai Zhou, Biao Qin, Xiaoyong Du	In this paper, we establish the link between pignistic and plausibility transformations by devising a belief-update framework for belief functions where plausibility transformation works on belief update while pignistic transformation operates on absolute belief.
649	When Will Negotiation Agents Be Able to Represent Us? The Challenges and Opportunities for Autonomous Negotiators	Tim Baarslag, Michael Kaisers, Enrico H. Gerding, Catholijn M. Jonker, Jonathan Gratch	We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust.
650	Algorithmic Bias in Autonomous Systems	David Danks, Alex John London	Algorithmic Bias in Autonomous Systems
651	Responsible Autonomy	Virginia Dignum	In this paper, we describe leading ethics theories and propose alternative ways to ensure ethical behavior by artificial systems.
652	Reinforcement Learning with a Corrupted Reward Channel	Tom Everitt, Victoria Krakovna, Laurent Orseau, Shane Legg	We formalise this problem as a generalised Markov Decision Problem called Corrupt Reward MDP.
653	A Goal Reasoning Agent for Controlling UAVs in Beyond-Visual-Range Air Combat	Michael W. Floyd, Justin Karneeb, Philip Moore, David W. Aha	We describe the Tactical Battle Manager (TBM), an intelligent agent that uses several integrated artificial intelligence techniques to control an autonomous unmanned aerial vehicle in simulated beyond-visual-range (BVR) air combat scenarios.
654	On Automating the Doctrine of Double Effect	Naveen Sundar Govindarajulu, Selmer Bringsjord	The goal in this paper is to automate DDE.
655	Achieving Coordination in Multi-Agent Systems by Stable Local Conventions under Community Networks	Shuyue Hu, Ho-fung Leung	In this paper, we provide a definition for local conventions, and propose two metrics measuring their strength and diversity.
656	Context-Based Reasoning on Privacy in Internet of Things	Nadin Kokciyan, Pinar Yolum	Accordingly, this paper proposes an approach where each entity finds out which contexts it is in based on information it gathers from other entities in the system.
657	Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning	Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller	We highlight the need for concrete evaluation metrics, propose example problems, and highlight possible solutions.
658	Should Robots be Obedient?	Smitha Milli, Dylan Hadfield-Menell, Anca Dragan, Stuart Russell	We investigate how this tradeoff is impacted by the way the robot infers the human’s preferences, showing that some methods err more on the side of obedience than others.
659	Privacy and Autonomous Systems	Jose M. Such	We discuss the problem of privacy in autonomous systems, introducing different conceptualizations and perspectives on privacy to assess the threats that autonomous systems may pose to privacy.
660	Online Decision-Making for Scalable Autonomous Systems	Kyle Hollins Wray, Stefan J. Witwicki, Shlomo Zilberstein	We present a general formal model called MODIA that can tackle a central challenge for autonomous vehicles (AVs), namely the ability to interact with an unspecified, large number of world entities.