Paper Digest: AAAI 2014 Highlights

July 26, 2014October 8, 2019 admin

The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2014, it is to be held in Quebec, Canada.

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

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

Paper Digest Team
team@paperdigest.org

TABLE 1: AAAI 2014 Papers

	Title	Authors	Highlight
1	TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation	Yang Bao, Hui Fang, Jie Zhang	In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts.
2	Context-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems	Chaochao Chen, Xiaolin Zheng, Yan Wang, Fuxing Hong, Zhen Lin	In this paper, we propose a novel context-aware hierarchical Bayesian method.
3	Improving Context and Category Matching for Entity Search	Yueguo Chen, Lexi Gao, Shuming Shi, Xiaoyong Du, Ji-Rong Wen	In this paper, we propose an approach of entity search by formalizing both context matching and category matching.
4	Machine Translation with Real-Time Web Search	Lei Cui, Ming Zhou, Qiming Chen, Dongdong Zhang, Mu Li	In contrast to existing methods, we propose a novel approach that treats machine translation as a web search task and utilizes the web on the fly to acquire translation knowledge.
5	Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation	Hui Fang, Yang Bao, Jie Zhang	Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation
6	Influence Maximization with Novelty Decay in Social Networks	Shanshan Feng, Xuefeng Chen, Gao Cong, Yifeng Zeng, Yeow Meng Chee, Yanping Xiang	In this paper, we consider the problem of influence maximization with novelty decay (IMND).
7	CoreCluster: A Degeneracy Based Graph Clustering Framework	Christos Giatsidis, Fragkiskos Malliaros, Dimitrios Thilikos, Michalis Vazirgiannis	In thisarticle, we present CoreCluster, an efficient graph clusteringframework based on the concept of graph degeneracy, that can be used along withany known graph clustering algorithm.
8	Experiments on Visual Information Extraction with the Faces of Wikipedia	Md. Kamrul Hasan, Christopher Joseph Pal	We present a series of visual information extraction experiments using the Faces of Wikipedia database – a new resource that we release into the public domain for both recognition and extraction research containing over 50,000 identities and 60,000 disambiguated images of faces. We focus on Wikipedia because the content is a Creative Commons resource and we provide our database to the community including registered faces, hand labeled and automated disambiguations, processed captions, meta data and evaluation protocols.
9	Online Social Spammer Detection	Xia Hu, Jiliang Tang, Huan Liu	In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing.
10	User Group Oriented Temporal Dynamics Exploration	Zhiting Hu, Junjie Yao, Bin Cui	This paper proposes GrosToT (Group Specific Topics-over-Time), a unified probabilistic model to infer latent user groups and temporal topics at the same time.
11	Predicting Emotions in User-Generated Videos	Yu-Gang Jiang, Baohan Xu, Xiangyang Xue	In this paper, we propose a comprehensive computational framework for predicting emotions in user-generated videos. We first introduce a rigorously designed dataset collected from popular video-sharing websites with manual annotations, which can serve as a valuable benchmark for future research.
12	How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance	Yong-Bin Kang, Jeff Z. Pan, Shonali Krishnaswamy, Wudhichart Sawangphol, Yuan-Fang Li	We demonstrate how they can be used to efficiently and accurately identify performance hotspots in a large and complex ontology, an otherwise very time-consuming and resource-intensive task.
13	Towards Scalable Exploration of Diagnoses in an Ontology Stream	Freddy Lecue	We address the problems of identifying, representing, exploiting and exploring the evolution of diagnoses representations.
14	ARIA: Asymmetry Resistant Instance Alignment	Sanghoon Lee, Seung-won Hwang	We study the problem of instance alignment between knowledge bases (KBs).
15	Learning Parametric Models for Social Infectivity in Multi-Dimensional Hawkes Processes	Liangda Li, Hongyuan Zha	To efficiently solve the resulting optimization problem, we employ the technique of alternating direction method of multipliers which allows independent updating of the individual coefficients by optimizing a surrogate function upper-bounding the original objective function.
16	Fraudulent Support Telephone Number Identification Based on Co-Occurrence Information on the Web	Xin Li, Yiqun Liu, Min Zhang, Shaoping Ma	In this paper, we propose an approach to identify fraudulent support telephone numbers on the Web based on the co-occurrence relations between telephone numbers that appear on SERPs.
17	Compact Aspect Embedding for Diversified Query Expansions	Xiaohua Liu, Arbi Bouchoucha, Alessandro Sordoni, Jian-Yun Nie	In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query.
18	Source Free Transfer Learning for Text Classification	PDF –> / 122	In this paper, we focus on the problem of auxiliary data retrieval, and propose a transfer learning framework that effectively selects helpful auxiliary data from an open knowledge space (e.g. the World Wide Web).
19	Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems	Boris Motik, Yavor Nenov, Robert Piro, Ian Horrocks, Dan Olteanu	We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog programs in centralised, main-memory, multi-core RDF systems.
20	Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations	Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, Ken-ichi Kawarabayashi	To address this issue, we propose a new method for the influence maximization problem.
21	Combining Heterogenous Social and Geographical Information for Event Recommendation	Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo, Binxing Fang	Therefore, we present a Bayesian latent factor model that can unify these data for event recommendation.
22	Stochastic Privacy	Adish Singla, Eric Horvitz, Ece Kamar, Ryen White	We introduce stochastic privacy, an approach to privacy centering on the simple concept of providing people with a guarantee that the probability that their personal data will be shared does not exceed a given bound.
23	Mapping Users across Networks by Manifold Alignment on Hypergraph	Shulong Tan, Ziyu Guan, Deng Cai, Xuzhen Qin, Jiajun Bu, Chun Chen	In this paper, we propose to utilize social structures to improve the mapping performance.
24	Acquiring Comparative Commonsense Knowledge from the Web	Niket Tandon, Gerard de Melo, Gerhard Weikum	In this paper, we first rely on open information extraction methods to obtain large amounts of comparisons from the Web.
25	Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems	Beidou Wang, Martin Ester, Jiajun Bu, Deng Cai	This paper presents the first algorithm to generate the most persuasive social explanation by recommending the optimal set of users to be put in the explanation.
26	Cross-Lingual Knowledge Validation Based Taxonomy Derivation from Heterogeneous Online Wikis	Zhigang Wang, Juanzi Li, Shuangjie Li, Mingyang Li, Jie Tang, Kuo Zhang, Kun Zhang	In this paper, we formulate the cross-lingual taxonomy derivation problem as the problem of cross-lingual taxonomic relation prediction.
27	Emotion Classification in Microblog Texts Using Class Sequential Rules	Shiyang Wen, Xiaojun Wan	This paper introduces a novel approach based on class sequential rules for emotion classification of microblog texts.
28	Quality-Based Learning for Web Data Classification	Ou Wu, Ruiguang Hu, Xue Mao, Weiming Hu	In this paper, the information quantity and quality of web data are described by quality-related factors such as text length and image quantity, and a new learning method is proposed to train classifiers based on quality-related factors.
29	Cross-View Feature Learning for Scalable Social Image Analysis	Wenxuan Xie, Yuxin Peng, Jianguo Xiao	In this paper, we propose a cross-view feature learning (CVFL) framework to handle the problem of social image analysis effectively and efficiently.
30	Capturing Difficulty Expressions in Student Online Q&A Discussions	Jaebong Yoo, Jihie Kim	We introduce a new application of online dialogue analysis: supporting pedagogical assessment of online Q&A discussions.
31	A Joint Optimization Model for Image Summarization Based on Image Content and Tags	Hongliang Yu, Zhi-Hong Deng, Yunlun Yang, Tao Xiong	Most existing summarization approaches use the visual-based features for image representation without considering tag information.In this paper, we propose a novel framework, named JOINT, which employs both image content and tag information to summarize images.
32	Recommendation by Mining Multiple User Behaviors with Group Sparsity	Ting Yuan, Jian Cheng, Xi Zhang, Shuang Qiu, Hanqing Lu	In this paper, we proposea novel recommendation model, the Group-Sparse MatrixFactorization (GSMF), which factorizes the ratingmatrices for multiple behaviors into the user and itemlatent factor space with group sparsity regularization.It can (1) select out the different subsets of latent factorsfor different behaviors, addressing that users’ decisionson different behaviors are determined by differentsets of factors;(2) model the dependence and independencebetween behaviors by learning the sharedand private factors for multiple behaviors automatically; (3) allow the shared factors between different behaviorsto be different, instead of all the behaviors sharingthe same set of factors.
33	Learning Temporal Dynamics of Behavior Propagation in Social Networks	Jun Zhang, Chaokun Wang, Jianmin Wang	In this paper we concentrate on the behavior modeling and systematically formulate the family of behavior propagation models (BPMs) including the static models (BP and IBP), and their discrete temporal variants (DBP and DIBP).
34	Trust Prediction with Propagation and Similarity Regularization	Xiaoming Zheng, Yan Wang, Mehmet A. Orgun, Youliang Zhong, Guanfeng Liu	In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties.
35	Synthesis of Geometry Proof Problems	Chris Alvin, Sumit Gulwani, Rupak Majumdar, Supratik Mukhopadhyay	This paper presents a semi-automated methodology for generating geometric proof problems of the kind found in a high-school curriculum.
36	GenEth: A General Ethical Dilemma Analyzer	Michael Anderson, Susan Leigh Anderson	To provide assistance in developing these ethical principles, we have developed GenEth, a general ethical dilemma analyzer that, through a dialog with ethicists, codifies ethical principles in any given domain.
37	Huffman Coding for Storing Non-Uniformly Distributed Messages in Networks of Neural Cliques	Bartosz Boguslawski, Vincent Gripon, Fabrice Seguin, Frédéric Heitzmann	In this work, we show the impact of non-uniformity on the performance of this recent model and we exploit the structure of the model to introduce several strategies to allow for efficient storage of non-uniform messages.
38	Where and Why Users “Check In”	Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan	In this study we analyze the check-in patterns in LBSN and observe significant temporal clustering of check-in activities.
39	A Machine Learning Approach to Musically Meaningful Homogeneous Style Classification	William Herlands, Ricky Der, Yoel Greenberg, Simon Levin	We present a supervised machine learning system which addresses the difficulty of differentiating between stylistically homogeneous composers using foundational elements of music, their complexity and interaction.
40	Programming by Example Using Least General Generalizations	Mohammad Raza, Sumit Gulwani, Natasa Milic-Frayling	We describe a novel domain specific language (DSL) that expresses transformations over XML structures describing richly formatted content, and a synthesis algorithm that generates a minimal program with respect to a natural subsumption ordering in our DSL.
41	Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes	Huawei Shen, Dashun Wang, Chaoming Song, Albert-László Barabási	Here we propose a generative probabilistic framework using a reinforced Poisson process to explicitly model the process through which individual items gain their popularity.
42	Joule Counting Correction for Electric Vehicles Using Artificial Neural Networks	Michael David Taylor	In this paper, we demonstrate a method for estimating battery remaining energy using real data collected from the Charge Car electric vehicle.
43	How Do Your Friends on Social Media Disclose Your Emotions?	Yang Yang, Jia Jia, Shumei Zhang, Boya Wu, Qicong Chen, Juanzi Li, Chunxiao Xing, Jie Tang	In this paper, we formally formalize the problem and propose a novel emotion learning method by jointly modeling images posted by social users and comments added by their friends.
44	Forecasting Potential Diabetes Complications	Yang Yang, Walter Luyten, Lu Liu, Marie-Francine Moens, Jie Tang, Juanzi Li	To address these challenges, we propose a novel probabilistic model called Sparse Factor Graph Model (SparseFGM).
45	k-CoRating: Filling Up Data to Obtain Privacy and Utility	Feng Zhang, Victor E. Lee, Ruoming Jin	In this paper, we propose $k$-coRating, a novel privacy-preserving model, to retain data privacy by replacing some null ratings with “well-predicted” scores.
46	Modeling Subjective Experience-Based Learning under Uncertainty and Frames	Hyung-il Ahn, Rosalind Picard	In this paper we computationally examine how subjective experience may help or harm the decision maker’s learning under uncertain outcomes, frames and their interactions.
47	The Importance of Cognition and Affect for Artificially Intelligent Decision Makers	Celso M. de Melo, Jonathan Gratch, Peter J. Carnevale	We present an experiment that tests the necessity of both for cooperation with agents.
48	Efficient Codes for Inverse Dynamics During Walking	Leif Johnson, Dana H Ballard	In this paper, we explore the use of efficient codes for representing information relevant to human movements during locomotion.
49	An Agent-Based Model Studying the Acquisition of a Language System of Logical Constructions	Josefina Sierra-Santibanez	This paper presents an agent-based model that studies the emergence and evolution of a language system of logical constructions, i.e. a vocabulary and a set of grammatical constructions that allow the expression of logical combinations of categories.
50	Large-Scale Analogical Reasoning	Vinay K. Chaudhri, Stijn J. Heymans, Adam Overholtzer, Aaron Spaulding, Michael Wessel	In this paper, relying on a well-curated biology KB, we present a specific implementation of comparison questions inspired by a general model of analogical reasoning.
51	Learning Compositional Sparse Models of Bimodal Percepts	Suren Kumar, Vikas Dhiman, Jason J. Corso	To that end, we propose a new approach to modeling bimodal percepts that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation.
52	Using Narrative Function to Extract Qualitative Information from Natural Language Texts	Clifton James McFate, Kenneth Forbus, Thomas Hinrichs	This paper describes a new approach, using narrative function to represent the higher-order relationships between the constituents of a sentence and between sentences in a discourse.
53	Confident Reasoning on Raven’s Progressive Matrices Tests	Keith McGreggor, Ashok Goel	Our technique introduces the calculation of confidence in an answer and the automatic adjustment of level of resolution if that confidence is insufficient.
54	Learning Goal-Oriented Hierarchical Tasks from Situated Interactive Instruction	Shiwali Mohan, John Laird	In this paper, we focus on learning goal-oriented tasks from situated interactive instructions.
55	Learning Unknown Event Models	Matthew Molineaux, David W. Aha	We investigate approaches for situated agents to detect surprises, discriminate among different forms of surprise, and hypothesize new models for the unknown events that surprised them.
56	Social Planning: Achieving Goals by Altering Others’ Mental States	Chris Pearce, Ben Meadows, Pat Langley, Mike Barley	In this paper, we discuss a computational approach to the cognitivetask of social planning.
57	Spatio-Temporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field	James Faghmous, Hung Nguyen, Matthew Le, Vipin Kumar	We introduce the notion of spatio-temporal consistency to identify eddies in a continuous spatio-temporal field, to simultaneously ensure that the features detected are both spatially and temporally consistent.
58	Placement of Loading Stations for Electric Vehicles: No Detours Necessary!	Stefan Ernst Funke, Andre Nusser, Sabine Storandt	In this paper, we consider the problem of placing as few loading stations as possible such that on any shortest path there are enough to guarantee sufficient energy supply.
59	A Region-Based Model for Estimating Urban Air Pollution	Arnaud Jutzeler, Jason Jingshi Li, Boi Faltings	In this paper, we propose a novel region-based Gaussian process model for estimating urban air pollution dispersion, and applied it to a large dataset of ultrafine particle (UFP) measurements collected from a network of sensors located on several trams in the city of Zurich.
60	Spatial Scan for Disease Mapping on a Mobile Population	Liang Lan, Vuk Malbasa, Slobodan Vucetic	In this paper, we propose a spatial scan statistic that is appropriate for disease mapping on mobile populations.
61	Challenges in Materials Discovery – Synthetic Generator and Real Datasets	Ronan Le Bras, Richard Bernstein, John M Gregoire, Santosh K Suram, Carla P Gomes, Bart Selman, R. Bruce van Dover	Hence, the goal of this paper is to stimulate the development of new computational techniques for the analysis of materials data, by bringing together the complimentary expertise of materials scientists and computer scientists. In collaboration with two major research laboratories in materials science, we provide the first publicly available dataset for the phase map identification problem.
62	Supervised Scoring with Monotone Multidimensional Splines	Abraham Othman	We consider the problem of how to generate scores in a setting where they should be weakly monotone (either non-increasing or non-decreasing) in their dimensions.
63	Efficient Buyer Groups for Prediction-of-Use Electricity Tariffs	Valentin Robu, Meritxell Vinyals, Alex Rogers, Nicholas R. Jennings	In this work we study the efficient (i.e. cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-of-use tariffs are available.
64	Intelligent System for Urban Emergency Management during Large-Scale Disaster	Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki	Facing these possible and unexpected disasters, urban emergency management has become the especially important issue for the whole governments around the world.In this paper, we present a novel intelligent system for urban emergency management during the large-scale disasters.
65	TacTex’13: A Champion Adaptive Power Trading Agent	Daniel Urieli, Peter Stone	This paper introduces TacTex’13, the champion agent from the inaugural competition in 2013.
66	Effective Management of Electric Vehicle Storage Using Smart Charging	Konstantina Valogianni, Wolfgang Ketter, John Collins, Dmitry Zhdanov	We propose an Adaptive Management of EV Storage (AMEVS) algorithm, implemented through a learning agent that acts on behalf of individual EV owners and schedules EV charging over a weekly horizon.
67	Rounded Dynamic Programming for Tree-Structured Stochastic Network Design	Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein	The underlying model describes phenomena that spread away from the root of a tree, for example, the spread of influence in a hierarchical organization or fish in a river network.
68	Contextually Supervised Source Separation with Application to Energy Disaggregation	Matt Wytock, J. Zico Kolter	We propose a new framework for single-channel source separation that liesbetween the fully supervised and unsupervised setting.
69	Modeling and Mining Spatiotemporal Patterns of Infection Risk from Heterogeneous Data for Active Surveillance Planning	Bo Yang, Hua Guo, Yi Yang, Benyun Shi, Xiaonong Zhou, Jiming Liu	In this paper, we raise the problem of active surveillance planning and provide an effective method to address it via modeling and mining spatiotemporal patterns of infection risks from heterogeneous data sources.
70	A Latent Variable Model for Discovering Bird Species Commonly Misidentified by Citizen Scientists	Jun Yu, Rebecca A. Hutchinson, Weng-Keen Wong	To accomplish this goal, we develop a latent variable graphical model that can identify groups of bird species that are often confused for each other by eBird participants.
71	Approximate Equilibrium and Incentivizing Social Coordination	Elliot Anshelevich, Shreyas Sekar	We study techniques to incentivize self-interested agents to form socially desirable solutions in scenarios where they benefit from mutual coordination.
72	Solving the Inferential Frame Problem in the General Game Description Language	Javier Romero Davila, Abdallah Saffidine, Michael Thielscher	We present a method by which general game players can transform any given game description into a representation that solves this problem.
73	Generating Content for Scenario-Based Serious-Games Using CrowdSourcing	Sigal Sina, Avi Rosenfeld, Sarit Kraus	To address this challenge, we present ScenarioGen, an automatic method for generating content about everyday activities through combining computer science techniques with the crowd.
74	Automatic Game Design via Mechanic Generation	Alexander Zook, Mark O. Riedl	As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations.
75	False-Name Bidding and Economic Efficiency in Combinatorial Auctions	Colleen Alkalay-Houlihan, Adrian Vetta	In contrast, we show that, provided the degree to which different goods are complementary is bounded (as is the case in many important, practical auctions), the VCG mechanism gives a constant efficiency guarantee.
76	On the Incompatibility of Efficiency and Strategyproofness in Randomized Social Choice	Haris Aziz, Florian Brandl, Felix Brandt	In this paper, we give an overview of common preference extensions, propose two new ones, and show that the above-mentioned incompatibility can be extended to various other notions of strategyproofness and efficiency in randomized social choice.
77	Fixing a Balanced Knockout Tournament	Haris Aziz, Serge Gaspers, Simon Mackenzie, Nicholas Mattei, Paul Stursberg, Toby Walsh	We present a memoization-based algorithm for the problem that is faster than previous approaches.
78	A Generalization of Probabilistic Serial to Randomized Social Choice	Haris Aziz, Paul Stursberg	We present the egalitarian simultaneous reservation social decision scheme – an extension of probabilistic serial to the more general setting of randomized social choice.
79	Simultaneous Cake Cutting	Eric Balkanski, Simina Brânzei, David Kurokawa, Ariel D. Procaccia	We introduce the simultaneous model for cake cutting (the fair allocation of a divisible good), in which agents simultaneously send messages containing a sketch of their preferences over the cake.
80	Lazy Defenders Are Almost Optimal against Diligent Attackers	Avrim Blum, Nika Haghtalab, Ariel D. Procaccia	We analytically demonstrate that in zero-sum security games, lazy defenders, who simply keep optimizing against perfectly informed attackers, are almost optimal against diligent attackers, who go to the effort of gathering a reasonable number of observations.
81	Extending Tournament Solutions	Felix Brandt, Markus Brill, Paul Harrenstein	In this paper, we argue that restricting attention to tournaments is justified by the existence of a conservative extension, which inherits most of the commonly considered properties from its underlying tournament solution.
82	The Fisher Market Game: Equilibrium and Welfare	Simina Brânzei, Yiling Chen, Xiaotie Deng, Aris Filos-Ratsikas, Søren Kristoffer Stiil Frederiksen, Jie Zhang	We show that the Fisher market game always has a pure Nash equilibrium, for buyers with linear, Leontief, and Cobb-Douglas utility functions, which are three representative classes of utility functions in the important Constant Elasticity of Substitution (CES) family.
83	Regret Transfer and Parameter Optimization	Noam Brown, Tuomas Sandholm	We propose a custom gradient descent algorithm that provably finds a locally optimal parameter vector while leveraging our warm-start theory to significantly save regret-matching iterations at each step.
84	Solving Imperfect Information Games Using Decomposition	Neil Burch, Michael Johanson, Michael Bowling	In particular, we present an algorithm for subgame solving which guarantees performance in the whole game, in contrast to existing methods which may have unbounded error.
85	Biased Games	Ioannis Caragiannis, David Kurokawa, Ariel D. Procaccia	We present a novel extension of normal form games that we call biased games.
86	Modal Ranking: A Uniquely Robust Voting Rule	Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah	Motivated by applications to crowdsourcing, we study voting rules that output a correct ranking of alternatives by quality from a large collection of noisy input rankings.
87	Mechanism Design for Scheduling with Uncertain Execution Time	Vincent Conitzer, Angelina Vidali	We study the problem where a task (or multiple unrelated tasks) must be executed, there are multiple machines/agents that can potentially perform the task, and our objective is to minimize the expected sum of the agents’ processing times.
88	Using Response Functions to Measure Strategy Strength	Trevor Davis, Neil Burch, Michael Bowling	In this work, we propose a class of response functions that can be used to measure the strength of a strategy.
89	New Models for Competitive Contagion	Moez Draief, Hoda Heidari, Michael Kearns	In this paper, we introduce and examine two new models for competitive contagion in networks, a game-theoretic generalization of the viral marketing problem.
90	Preference Elicitation and Interview Minimization in Stable Matchings	Joanna Drummond, Craig Boutilier	We develop a new model for representing and assessing agent preferences that accommodates both forms of information and (heuristically) minimizes the number of queries and interviews required to determine a stable matching.
91	A Characterization of the Single-Peaked Single-Crossing Domain	Edith Elkind, Piotr Faliszewski, Piotr Skowron	We investigate elections that are simultaneously single-peaked and single-crossing (SPSC).
92	On Detecting Nearly Structured Preference Profiles	Edith Elkind, Martin Lackner	In this paper, we show that these problems admit efficient approximation algorithms.
93	Binary Aggregation by Selection of the Most Representative Voters	Ulle Endriss, Umberto Grandi	Instead, we explore a class of low-complexity aggregation rules that select the most representative voter in any given situation and return that voter’s choice as the outcome.
94	On the Axiomatic Characterization of Runoff Voting Rules	Rupert Freeman, Markus Brill, Vincent Conitzer	We characterize runoff rules that are based on scoring rules using two axioms: a weakening of local independence of irrelevant alternatives and a variant of population-consistency.
95	Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games	Sam Ganzfried, Tuomas Sandholm	We present the first algorithm for computing potential-aware imperfect-recall abstractions using earth mover’s distance.
96	Mechanism Design for Mobile Geo-Location Advertising	Nicola Gatti, Marco Rocco, Sofia Ceppi, Enrico H. Gerding	To this end, we introduce, for the first time, a user model and suitable economic mechanisms which take these factors into account.
97	Voting with Rank Dependent Scoring Rules	Judy Goldsmith, Jérôme Lang, Nicholas Mattei, Patrice Perny	We study some properties of these rules, and show, empirically, that certain RDSRs are less manipulable than Borda voting, across a variety of statistical cultures.
98	Increasing VCG Revenue by Decreasing the Quality of Items	Mingyu Guo, Argyrios Deligkas, Rahul Savani	We consider the following two revenue maximization problems under VCG: finding an optimal way to mark down items by reducing their quality levels, and finding an optimal set of items to burn.
99	A Control Dichotomy for Pure Scoring Rules	Edith Hemaspaandra, Lane A. Hemaspaandra, Henning Schnoor	In particular, for constructive control by adding voters (CCAV), we show that CCAV is solvable in polynomial time for k-approval with k<=3, k-veto with k<=2, every pure scoring rule in which only the two top-rated candidates gain nonzero scores, and a particular rule that is a “hybrid” of 1-approval and 1-veto.
100	A Multiarmed Bandit Incentive Mechanism for Crowdsourcing Demand Response in Smart Grids	Shweta Jain, Balakrishnan Narayanaswamy, Y. Narahari	Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand.
101	Envy-Free Division of Sellable Goods	Jeremy Karp, Aleksandr M. Kazachkov, Ariel D. Procaccia	We study the envy-free allocation of indivisible goods between two players.
102	Betting Strategies, Market Selection, and the Wisdom of Crowds	Willemien Kets, David M. Pennock, Rajiv Sethi, Nisarg Shah	We investigate the limiting behavior of trader wealth and prices in a simple prediction market with a finite set of participants having heterogeneous beliefs.
103	Incomplete Preferences in Single-Peaked Electorates	Martin Lackner	Despite this computational hardness result, we find four polynomial-time algorithms for reasonably restricted settings.
104	Item Bidding for Combinatorial Public Projects	Evangelos Markakis, Orestis Telelis	We present and analyze a mechanism for the Combinatorial Public Project Problem (CPPP).
105	Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty	Thanh Hong Nguyen, Amulya Yadav, Bo An, Milind Tambe, Craig Boutilier	In contrast, in this work we propose the use of the less conservative minimax regret decision criterion for such payoff-uncertain SSGs and present the first algorithms for computing minimax regret for SSGs.
106	On the Structure of Synergies in Cooperative Games	Ariel D. Procaccia, Nisarg Shah, Max Lee Tucker	We investigate synergy, or lack thereof, between agents in cooperative games, building on the popular notion of Shapley value.
107	Incentives for Truthful Information Elicitation of Continuous Signals	Goran Radanovic, Boi Faltings	We propose a novel mechanism that elicits both private signals and beliefs.
108	Equilibria in Epidemic Containment Games	Sudip Saha, Abhijin Adiga, Anil Kumar S. Vullikanti	We show in this model that pure Nash equilibria (NE) always exist, and can be found by a best response strategy.
109	Bounding the Support Size in Extensive Form Games with Imperfect Information	Martin Schmid, Matej Moravcik, Milan Hladik	We present a dependency between the level of uncertainty and the minimum support size.
110	Two Case Studies for Trading Multiple Indivisible Goods with Indifferences	Akihisa Sonoda, Etsushi Fujita, Taiki Todo, Makoto Yokoo	In this paper we investigate mechanisms for exchange models where each agent is initially endowed with a set of goods and may have indifferences on distinct bundles of goods, and monetary transfers are not allowed.
111	Beat the Cheater: Computing Game-Theoretic Strategies for When to Kick a Gambler out of a Casino	Troels Bjerre Sørensen, Melissa Dalis, Joshua Letchford, Dmytro Korzhyk, Vincent Conitzer	In this paper, we address the following question: Based solely on a gambler’s track record,when is it optimal for the casino to kick the gambler out?
112	Strategyproof Exchange with Multiple Private Endowments	Taiki Todo, Haixin Sun, Makoto Yokoo	Our objective in this paper is to analyze the effect of such private ownership in exchange economies with multiple endowments.
113	A Strategy-Proof Online Auction with Time Discounting Values	Fan Wu, Junming Liu, Zhenzhe Zheng, Guihai Chen	In this paper, we investigate online auctions with time discounting values, in contrast to the flat values studied in most of existing work.
114	Incentivizing High-Quality Content from Heterogeneous Users: On the Existence of Nash Equilibrium	Yingce Xia, Tao Qin, Nenghai Yu, Tie-Yan Liu	In this work, we consider the following setting: (1) the users are heterogeneous and each of them has a private type indicating the best quality of the content he/she can generate; (2) all the users share a fixed total reward.
115	Game-Theoretic Resource Allocation for Protecting Large Public Events	Yue Yin, Bo An, Manish Jain	We propose SCOUT-A, which makes assumptions on relocation cost, exploits payoff representation and computes optimal solutions efficiently.
116	Relaxation Search: A Simple Way of Managing Optional Clauses	Fahiem Bacchus, Jessica Davies, Maria Tsimpoukelli, George Katsirelos	We demonstrate how relaxation search can be used to solve MAXSAT and to compute Minimum Correction Sets.
117	Parallel Restarted Search	Andre Cire, Serdar Kadioglu, Meinolf Sellmann	We consider the problem of parallelizing restarted backtrack search.
118	Designing Fast Absorbing Markov Chains	Stefano Ermon, Carla Gomes, Ashish Sabharwal, Bart Selman	Given a graph with some specified sink nodes and an initial probability distribution,we consider the problem of designing an absorbing Markov Chain that minimizes the time required to reach a sink node, by selecting transition probabilities subject to some natural regularity constraints.
119	Simpler Bounded Suboptimal Search	Matthew Hatem, Wheeler Ruml	In this paper, we present simplified variants of EES (SEES) and an earlier algorithm, Aepsilon (SAepsilon), that use different implementations of the same motivating ideas to significantly reduce search overhead and implementation complexity.
120	Elimination Ordering in Lifted First-Order Probabilistic Inference	Seyed Mehran Kazemi, David Poole	In this paper, we show that these heuristics are inefficient for relational models, because they fail to consider the population sizes associated with logical variables.
121	Exponential Deepening A* for Real-Time Agent-Centered Search	Guni Sharon, Ariel Felner, Nathan Sturtevant	We study theIterative Deepening (ID) approach for solving RTACS andintroduce Exponential Deepening A* (EDA*), an RTACS algorithmwhere the threshold between successive Depth-Firstcalls is increased exponentially.
122	Identifying Hierarchies for Fast Optimal Search	Tansel Uras, Sven Koenig	In this paper, we generalize this partitioning process to any undirected graph and show that it can be recursively applied to generate more than two levels, which reduces the size of the graph being searched even further.
123	Worst-Case Solution Quality Analysis When Not Re-Expanding Nodes in Best-First Search	Richard Anthony Valenzano, Nathan R. Sturtevant, Jonathan Schaeffer	In this paper, we formally show that the loss in solution quality can be bounded based on the amount of inconsistency along optimal solution paths.
124	Sparse Learning for Stochastic Composite Optimization	Weizhong Zhang, Lijun Zhang, Yao Hu, Rong Jin, Deng Cai, Xiaofei He	In this paper, we focus on Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution.
125	Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation	Marina Boia, Claudiu Cristian Musat, Boi Faltings	We describe a novel task design that allows us to crowdsource this knowledge through Amazon Mechanical Turk with high quality.
126	Signals in the Silence: Models of Implicit Feedback in a Recommendation System for Crowdsourcing	Christopher H Lin, Ece Kamar, Eric Horvitz	We present methods that enable a system to leverage implicit signals about task preferences.
127	Leveraging Fee-Based, Imperfect Advisors in Human-Agent Games of Trust	Cody Buntain, Amos Azaria, Sarit Kraus	This paper explores whether the addition of costly, imperfect, and exploitable advisors to Berg’s investment game enhances or detracts from investor performance in both one-shot and multi-round interactions.We then leverage our findings to develop an automated investor agent that performs as well as or better than humans in these games.To gather this data, we extended Berg’s game and conducted a series of experiments using Amazon’s Mechanical Turk to determine how humans behave in these potentially adversarial conditions.Our results indicate that, in games of short duration, advisors do not stimulate positive behavior and are not useful in providing actionable advice.In long-term interactions, however, advisors do stimulate positive behavior with significantly increased investments and returns.By modeling human behavior across several hundred participants, we were then able to develop agent strategies that maximized return on investment and performed as well as or significantly better than humans.In one-shot games, we identified an ideal investment value that, on average, resulted in positive returns as long as advisor exploitation was not allowed.For the multi-round games, our agents relied on the corrective presence of advisors to stimulate positive returns on maximum investment.
128	Can Agent Development Affect Developer’s Strategy?	Avshalom Elmalech, David Sarne, Noa Agmon	In this paper we show that PDA development has an important side effect that has not been addressed to date — the process that merely attempts to capture one’s strategy is also likely to affect the developer’s strategy.
129	Ordering Effects and Belief Adjustment in the Use of Comparison Shopping Agents	Chen Hajaj, Noam Hazon, David Sarne	In this paper we suggest a complementary approach that improves the attractiveness of a CSA by presenting the prices to the user in a specific intelligent manner, which is based on known cognitive-biases.
130	A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback	Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts	This paper introduces two novel algorithms for learning behaviors from human-provided rewards.
131	Dramatis: A Computational Model of Suspense	Brian O'Neill, Mark Riedl	We introduce Dramatis, a computational model of suspense based on a reformulation of a psychological definition of the suspense phenomenon.
132	Sketch Recognition with Natural Correction and Editing	Jie Wu, Changhu Wang, Liqing Zhang, Yong Rui	In this paper, we target at the problem of sketch recognition.
133	Role-Aware Conformity Modeling and Analysis in Social Networks	Jing Zhang, Jie Tang, Honglei Zhuang, Cane Wing-Ki Leung, Juanzi Li	In this paper, we study how the conformity tendency of a person changes with her role, as defined by her structural properties in a social network.
134	Managing Change in Graph-Structured Data Using Description Logics	Shqiponja Ahmetaj, Diego Calvanese, Magdalena Ortiz, Mantas Simkus	In this paper we consider the setting of graph-structured data that evolves as a result of operations carried out by users or applications.
135	The Computational Complexity of Structure-Based Causality	Gadi Aleksandrowicz, Hana Chockler, Joseph Y. Halpern, Alexander Ivrii	As we show, this modification has a nontrivial impact on the complexity of computing actual cause.
136	Pathway Specification and Comparative Queries: A High Level Language with Petri Net Semantics	Saadat Anwar, Chitta Baral	In this paper, we present overview of such a system we developed and an English-like high level language to express pathways and queries.
137	PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains	Vaishak Belle, Hector Levesque	This paper proposes a new language and an implemented system, called PREGO, based on the situation calculus, that is able to reason effectively about degrees of belief against noisy sensors and effectors in continuous domains.
138	Querying Inconsistent Description Logic Knowledge Bases under Preferred Repair Semantics	Meghyn Bienvenu, Camille Bourgaux, François Goasdoué	In this paper, we study variants of two popular inconsistency-tolerant semantics obtained by replacing classical repairs by various types of preferred repair.
139	Capturing Relational Schemas and Functional Dependencies in RDFS	Diego Calvanese, Wolfgang Fischl, Reinhard Pichler, Emanuel Sallinger, Mantas Simkus	In this work, we propose an enrichment of the direct mapping to make it more faithful by transferring also semantic information present in the relational schema from the relational world to the RDF world.
140	Exploring the Boundaries of Decidable Verification of Non-Terminating Golog Programs	Jens Classen, Martin Liebenberg, Gerhard Lakemeyer, Benjamin Zarriess	In this paper, we show how decidability can be obtained by suitably restricting the underlying base logic, the effect axioms for primitive actions, and the use of actions within GOLOG programs.
141	Data Quality in Ontology-based Data Access: The Case of Consistency	Marco Console, Maurizio Lenzerini	In this paper we argue that OBDA, besides querying data, provides the right principles for devising a formal approach to data quality.
142	Reasoning on LTL on Finite Traces: Insensitivity to Infiniteness	Giuseppe De Giacomo, Riccardo De Masellis, Marco Montali	In this paper we study when an LTL formula on finite traces (LTLf formula) is insensitive to infiniteness, that is, it can be correctly handled as a formula on infinite traces under the assumption that at a certain point the infinite trace starts repeating an end event forever, trivializing all other propositions to false.
143	A Tractable Approach to ABox Abduction over Description Logic Ontologies	Jianfeng Du, Kewen Wang, Yi-Dong Shen	To reduce the number of explanations that need to be computed, we introduce a special kind of minimal explanations called representative explanations from which all minimal explanations can be retrieved.
144	Exploiting Support Sets for Answer Set Programs with External Evaluations	Thomas Eiter, Michael Fink, Christoph Redl, Daria Stepanova	In this paper we consider HEX-programs that provide external atoms as a bidirectional interface to external sources and present a novel evaluation method based on support sets, which informally are portions of the input to an external atom that will determine its output for any completion of the partial input.
145	A Knowledge Compilation Map for Ordered Real-Valued Decision Diagrams	Hélène Fargier, Pierre Marquis, Alexandre Niveau, Nicolas Schmidt	This paper contributes to filling this gap and completing previous results about the time and space efficiency of VDD languages, thus leading to a knowledge compilation map for real-valued functions.
146	The Complexity of Reasoning with FODD and GFODD	Benjamin J. Hescott, Roni Khardon	In this paper, we study the complexity of the evaluation problem, the satiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability.
147	Elementary Loops Revisited	Jianmin Ji, Hai Wan, Peng Xiao, Ziwei Huo, Zhanhao Xiao	This paper proposes an alternative definition of elementary loops and identify a subclass of elementary loops, called proper loops.
148	A Constructive Argumentation Framework	Souhila Kaci, Yakoub Salhi	We develop constructive argumentation framework.
149	Datalog Rewritability of Disjunctive Datalog Programs and its Applications to Ontology Reasoning	Mark Kaminski, Yavor Nenov, Bernardo Cuenca Grau	We study the problem of rewriting a disjunctive datalog program into plain datalog.
150	Qualitative Reasoning with Modelica Models	Matthew Evans Klenk, Johan de Kleer, Daniel Bobrow, Bill Janssen	The contribution of this paper is a sound and effective mapping between Modelica and qualitative reasoning.
151	A Parameterized Complexity Analysis of Generalized CP-Nets	Martin Kronegger, Martin Lackner, Andreas Pfandler, Reinhard Pichler	In this work, we employ the framework of parameterized complexity to achieve two goals: First, we want to gain a deeper understanding of the complexity of GCP-nets.
152	The Most Uncreative Examinee: A First Step toward Wide Coverage Natural Language Math Problem Solving	Takuya Matsuzaki, Hidenao Iwane, Hirokazu Anai, Noriko H Arai	Using the prototype system as a reference point, we analyzed real university entrance examination problems from the viewpoint of end-to-end automated reasoning.
153	Computing General First-Order Parallel and Prioritized Circumscription	Hai Wan, Zhanhao Xiao, Zhenfeng Yuan, Heng Zhang, Yan Zhang	We propose linear translations from general first-order circumscription to first-order theories under stable model semantics over arbitrary structures, including Tr_v for parallel circumscription and Tr^s_v for conjunction of parallel circumscriptions (further for prioritized circumscription).
154	Knowledge Graph Embedding by Translating on Hyperplanes	Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen	To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it.
155	Abduction Framework for Repairing Incomplete EL Ontologies: Complexity Results and Algorithms	Fang Wei-Kleiner, Zlatan Dragisic, Patrick Lambrix	In this paper we consider the problem of repairing missing is-a relations in ontologies.
156	Local-to-Global Consistency Implies Tractability of Abduction	Michal Wrona	Our main contribution is an algorithm that under some natural conditions decides Abduction(Gamma, Hyp, M) in P when Gamma has local-to-global consistency.
157	Using Model-Based Diagnosis to Improve Software Testing	Tom Zamir, Roni Tzvi Stern, Meir Kalech	We propose a combination of AI techniques to improve softwaretesting.
158	Pay-As-You-Go OWL Query Answering Using a Triple Store	Yujiao Zhou, Yavor Nenov, Bernardo Cuenca Grau, Ian Horrocks	We present an enhanced hybrid approach to OWL query answering that combines an RDF triple-store with an OWL reasoner in order to provide scalable pay-as-you-go performance.
159	Contraction and Revision over DL-Lite TBoxes	Zhiqiang Zhuang, Zhe Wang, Kewen Wang, Guilin Qi	Such elimination and incorporation are formalised as the operations of contraction and revision in belief change.In this paper, we deal with contraction and revision for the DL-Lite family through a model-theoretic approach.Standard DL semantics yields infinite numbers of models for DL-Lite TBoxes, thus it is not practical to develop algorithms for contraction and revision that involve DL models.
160	A Spatially Sensitive Kernel to Predict Cognitive Performance from Short-Term Changes in Neural Structure	M. Hidayath Ansari, Michael H. Coen, Barbara B Bendlin, Mark A Sager, Sterling C Johnson	This paper introduces a novel framework for performing machine learning onlongitudinal neuroimaging datasets.
161	Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques	Márcia L. Baptista, Anjie Fang, Helmut Prendinger, Rui Prada, Yohei Yamaguchi	To address these issues, we propose a novel approach that relies on a combination of data mining techniques.
162	A Convex Formulation for Semi-Supervised Multi-Label Feature Selection	Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang	In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets.
163	Predicting Postoperative Atrial Fibrillation from Independent ECG Components	Chih-Chun Chia, James Blum, Zahi Karam, Satinder Singh, Zeeshan Syed	As a first step, we explore an eigen-decomposition approach that partitions ECG signals into atrial and ventricular components by exploiting knowledge of the underlying cardiac cycle.
164	Online Portfolio Selection with Group Sparsity	Puja Das, Nicholas Johnson, Arindam Banerjee	In this paper, we propose an online portfolio selection algorithm that can take advantage of sector information through the use of a group sparsity inducing regularizer while making lazy updates to the portfolio.
165	Latent Low-Rank Transfer Subspace Learning for Missing Modality Recognition	Zhengming Ding, Shao Ming, Yun Fu	We consider an interesting problem in this paper that uses transfer learning in two directions to compensate missing knowledge from the target domain.
166	On the Challenges of Physical Implementations of RBMs	Vincent Dumoulin, Ian J Goodfellow, Aaron Courville, Yoshua Bengio	Our simulations are based on the D-Wave Two computer, but the issues we investigate arise in most forms of physical computation.Our findings suggest that designers of new physical computing hardware and algorithms for physical computers should focus their efforts on overcoming the limitations imposed by the topology restrictions of currently existing physical computers.
167	SOML: Sparse Online Metric Learning with Application to Image Retrieval	Xingyu Gao, Steven C.H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li	In thispaper, we propose a novel Sparse Online Metric Learning (SOML)scheme for learning sparse distance functions from large-scalehigh-dimensional data and explore its application to imageretrieval.
168	Calibration-Free BCI Based Control	Jonathan Grizou, Iñaki Iturrate, Luis Montesano, Pierre-Yves Oudeyer, Manuel Lopes	This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task.
169	User Intent Identification from Online Discussions Using a Joint Aspect-Action Topic Model	Ghasem Heyrani Nobari, Chua Tat-Seng	In this paper, we propose a novel unsupervised generative model to derive aspect-action pairs from online discussions.
170	Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery	Chengcheng Jia, Guoqiang Zhong, Yun Fu	In this paper, we propose a low-rank tensor completion method for action classification, as well as image recovery.
171	Ranking Tweets by Labeled and Collaboratively Selected Pairs with Transitive Closure	Shenghua Liu, Xueqi Cheng, Fangtao Li	In this paper, we propose a new collaborative semi-supervised SVM ranking model (CSR-TC)with consideration of the order conflict.
172	Automatic Construction and Natural-Language Description of Nonparametric Regression Models	James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua Tenenbaum, Zoubin Ghahramani	This paper presents the beginnings of an automatic statistician, focusing on regression problems.
173	Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization	Canyi Lu, Yunchao Wei, Zhouchen Lin, Shuicheng Yan	This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for solving a general problem, which involves a large body of nonconvex sparse and structured sparse related problems.
174	Direct Semantic Analysis for Social Image Classification	Zhiwu Lu, Liwei Wang, Ji-Rong Wen	This paper presents a direct semantic analysis method for learning the correlation matrix between visual and textual words from socially tagged images.
175	Discovering Better AAAI Keywords via Clustering with Community-Sourced Constraints	Kelly H. Moran, Byron C. Wallace, Carla E. Brodley	Selecting good conference keywords is important because they often determine the composition of review committees and hence which papers are reviewed by whom.
176	Learning Latent Engagement Patterns of Students in Online Courses	Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daume III, Lise Getoor	In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues.
177	Generalized Higher-Order Tensor Decomposition via Parallel ADMM	Fanhua Shang, Yuanyuan Liu, James Cheng	By considering the low-rank structure of the observed tensor, we analyze the equivalent relationship of the trace norm between a low-rank tensor and its core tensor.
178	Doubly Regularized Portfolio with Risk Minimization	Weiwei Shen, Jun Wang, Shiqian Ma	In this paper, we propose a doubly regularized portfolio that provides a modest but effective solution to the above difficulty.
179	Learning Deep Representations for Graph Clustering	Fei Tian, Bin Gao, Qing Cui, Enhong Chen, Tie-Yan Liu	In this work, we explore the possibility of employing deep learning in graph clustering.
180	Agent Behavior Prediction and Its Generalization Analysis	Fei Tian, Haifang Li, Wei Chen, Tao Qin, Enhong Chen, Tie-Yan Liu	We propose a novel technique that transforms the original time-variant MCRE into a higher-dimensional time-homogeneous Markov chain, which is easier to deal with.
181	Evaluating Trauma Patients: Addressing Missing Covariates with Joint Optimization	Alex Van Esbroeck, Satinder Singh, Ilan Rubinfeld, Zeeshan Syed	Missing values are a common problem when applying classification algorithms to real-world medical data.
182	Identifying Differences in Physician Communication Styles with a Log-Linear Transition Component Model	Byron C Wallace, Issa J Dahabreh, Thomas A Trikalinos, Michael Barton Laws, Ira Wilson, Eugene Charniak	We propose a novel approach toward this end in which we model speech act transitions in conversations via a log-linear model incorporating physician specific components.
183	Robust Distance Metric Learning in the Presence of Label Noise	Dong Wang, Xiaoyang Tan	Particularly, we analyze the effect of label noise on the derivative of likelihood with respect to the transformation matrix, and propose to model the conditional probability of the true label of each point so as to reduce that effect.
184	Globally and Locally Consistent Unsupervised Projection	Hua Wang, Feiping Nie, Heng Huang	In this paper, we propose an unsupervised projection method for feature extraction to preserve both global and local consistencies of the input data in the projected space.
185	Adaptive Knowledge Transfer for Multiple Instance Learning in Image Classification	Qifan Wang, Lingyun Ruan, Luo Si	Motivated by the empirical success of transfer learning, this paper proposes a novel approach of Adaptive Knowledge Transfer for Multiple Instance Learning (AKT-MIL) in image classification.
186	Privacy and Regression Model Preserved Learning	Jinfeng Yi, Jun Wang, Rong Jin	In this paper, we propose a novel matrix completion based framework that aims to tackle two challenging issues simultaneously: i) handling missing and noisy sensitive data, and ii) preserving the privacy of the sensitive data during the learning process.
187	Decomposing Activities of Daily Living to Discover Routine Clusters	Onur Yürüten, Jiyong Zhang, Pearl Pu	The modern sensor technology helps us collect time series data for activities of daily living (ADLs), which in turn can be used to infer broad patterns, such as common daily routines.
188	Feature Selection at the Discrete Limit	Miao Zhang, Chris Ding, Ya Zhang, Feiping Nie	In this paper, we propose to use L2,p norm for feature selection with emphasis on small p.
189	Hybrid Singular Value Thresholding for Tensor Completion	Xiaoqin Zhang, Zhengyuan Zhou, Di Wang, Yi Ma	In this paper, we study the low-rank tensor completion problem, where a high-order tensor with missing entries is given and the goal is to complete the tensor.
190	Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks	Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu	Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN).
191	Accurate Integration of Aerosol Predictions by Smoothing on a Manifold	Shuai Zheng, James Kwok	Using a probabilistic approach, we impose this smoothness constraint by a Gaussian random field on the Earth’s surface, which can be considered as a two-dimensional manifold.
192	Dynamic Multi-Agent Task Allocation with Spatial and Temporal Constraints	Sofia Amador, Steven Okamoto, Roie Zivan	We propose FMC_TA, a novel task allocation algorithm that allows tasks to be easily sequenced to yield high-quality solutions.
193	Robust Winners and Winner Determination Policies under Candidate Uncertainty	Craig Boutilier, Jérôme Lang, Joel Oren, Héctor Palacios	Assuming a distribution over availability, and costs for availability tests/queries, we describe algorithms for computing optimal query policies, which minimize the expected cost of determining true winners.
194	Prices Matter for the Parameterized Complexity of Shift Bribery	Robert Bredereck, Jiehua Chen, Piotr Faliszewski, André Nichterlein, Rolf Niedermeier	We study the parameterized computational complexity of Shift Bribery with respect to a number of parameters (pertaining to the nature of the solution sought and the size of the election) and several classes of price functions.
195	The Computational Rise and Fall of Fairness	John P Dickerson, Jonathan Goldman, Jeremy Karp, Ariel D Procaccia, Tuomas Sandholm	We investigate the existence of envy-free allocations of indivisible goods, that is, allocations where each player values her own allocated set of goods at least as highly as any other player’s allocated set of goods.
196	Multi-Organ Exchange: The Whole Is Greater than the Sum of its Parts	John P Dickerson, Tuomas Sandholm	In this paper, we begin by proposing the idea of liver exchange, and show on demographically accurate data that vetted kidney exchange algorithms can be adapted to clear such an exchange at the nationwide level.
197	On Computing Optimal Strategies in Open List Proportional Representation: The Two Parties Case	Ning Ding, Fangzhen Lin	In this paper, we assume that there are just two parties in the election, and that the number of votes that a list would get is the sum of the numbers of votes that the candidates in the list would get if each of them would go alone in the election.
198	Symbolic Model Checking Epistemic Strategy Logic	Xiaowei Huang, Ron van der Meyden	This paper presents a symbolic BDD-based model checking algorithm for an epistemic strategy logic with observational semantics.
199	Internally Stable Matchings and Exchanges	Yicheng Liu, Pingzhong Tang, Wenyi Fang	Our contribu-tions are as follows: for both pairwise matchings and limited-length exchanges, for both unweighted and weighted graph-s, (1) we prove desirable tight social welfare bounds; (2) weanalyze the computational complexity for clearing the match-ings and exchanges.
200	Congestion Games for V2G-Enabled EV Charging	Benny Lutati, Vadim Levit, Tal Grinshpoun, Amnon Meisels	A compact representation and an algorithm that enable efficient best-response search are presented.
201	Decentralized Multi-Agent Reinforcement Learning in Average-Reward Dynamic DCOPs	Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang	Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments in the current time step; (ii) We introduce two distributed reinforcement learning algorithms, the Distributed RVI Q-learning algorithm and the Distributed R-learning algorithm, that balance exploration and exploitation to solve MD-DCOPs in an online manner; and (iii) We empirically evaluate them against an existing multi-arm bandit DCOP algorithm on dynamic DCOPs.
202	Online (Budgeted) Social Choice	Joel Oren, Brendan Lucier	In contrast, if the agents arrive in random order, we present a $(1 – \frac{1}{e} – o(1))$-approximatealgorithm, matching a lower bound for the off-line problem.We show that improved performance is possible for natural input distributionsor scoring rules.
203	A Game-Theoretic Analysis of Catalog Optimization	Joel Oren, Nina Narodytska, Craig Boutilier	We develop a game-theoretic model for analyzing the vendor catalog optimization problem in the face of competing vendors.
204	Theory of Cooperation in Complex Social Networks	Bijan Ranjbar-Sahraei, Haitham Bou Ammar, Daan Bloembergen, Karl Tuyls, Gerhard Weiss	This paper presents a theoretical as well as empirical study on the evolution of cooperation on complex social networks, following the continuous action iterated prisoner’s dilemma (CAIPD) model.
205	Multiagent Metareasoning through Organizational Design	Jason Sleight, Edmund H. Durfee	We describe an automated organizational design process that can approximately solve our organizational design problem via incremental search, and present techniques that efficiently estimate the incremental impact of a candidate organizational influence.
206	Give a Hard Problem to a Diverse Team: Exploring Large Action Spaces	Leandro Soriano Marcolino, Haifeng Xu, Albert Xin Jiang, Milind Tambe, Emma Bowring	Hence, we present a new model of diversity for teams, that is more general than previous models.
207	Regret-Based Multi-Agent Coordination with Uncertain Task Rewards	Feng Wu, Nicholas R. Jennings	To address this, we propose a novel decentralized algorithm that incorporates Max-Sum with iterative constraint generation to solve the problem by passing messages among agents.
208	Solving Zero-Sum Security Games in Discretized Spatio-Temporal Domains	Haifeng Xu, Fei Fang, Albert Xin Jiang, Vincent Conitzer, Shaddin Dughmi, Milind Tambe	Our framework enables efficient computation of a minimax strategy when the problem admits a polynomial-time oracle.
209	Scalable Complex Contract Negotiation with Structured Search and Agenda Management	Xiaoqin Shelley Zhang, Mark Klein, Ivan Marsa-Maestre	To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator.
210	SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis	Erik Cambria, Daniel Olsher, Dheeraj Rajagopal	Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of “energy flows” to connect various parts of extended common and common-sense knowledge representations to one another.
211	Joint Morphological Generation and Syntactic Linearization	Linfeng Song, Yue Zhang, Kai Song, Qun Liu	In this paper, we study joint morphological generation and linearization, making use of word order and inflections information for both tasks and reducing error propagation.
212	Improving Domain-independent Cloud-Based Speech Recognition with Domain-Dependent Phonetic Post-Processing	Johannes Twiefel, Timo Baumann, Stefan Heinrich, Stefan Wermter	We present a versatile post-processing technique based on phonetic distance that integrates domain knowledge with open-domain ASR results, leading to improved ASR performance.
213	Adaptive Multi-Compositionality for Recursive Neural Models with Applications to Sentiment Analysis	Li Dong, Furu Wei, Ming Zhou, Ke Xu	We present a general framework to model each semantic composition as a distribution over these composition functions.
214	Collaborative Models for Referring Expression Generation in Situated Dialogue	Rui Fang, Malcolm Doering, Joyce Y. Chai	Both models, instead of generating a single referring expression to describe a target object as in the previous work, generate multiple small expressions that lead to the target object with the goal of minimizing the collaborative effort.
215	Prediction of Helpful Reviews Using Emotions Extraction	Lionel Martin, Pearl Pu	As the second contribution, we propose an evaluation framework comparing three different real-world datasets extracted from the most well-known product review websites.
216	Unsupervised Alignment of Natural Language Instructions with Video Segments	Iftekhar Naim, Young Chol Song, Qiguang Liu, Henry Kautz, Jiebo Luo, Daniel Gildea	We propose an unsupervised learning algorithm for automatically inferring the mappings between English nouns and corresponding video objects.
217	Learning Scripts as Hidden Markov Models	John Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Fern, Thomas G. Dietterich	This paper proposes the first formal frameworkfor scripts based on Hidden Markov Models (HMMs).
218	Learning Word Representation Considering Proximity and Ambiguity	Lin Qiu, Yong Cao, Zaiqing Nie, Yong Yu, Yong Rui	In this paper, we propose Proximity-Ambiguity Sensitive (PAS) models (i.e. PAS CBOW and PAS Skip-gram) to produce high quality distributed representations of words considering both word proximity and ambiguity.
219	On Dataless Hierarchical Text Classification	Yangqiu Song, Dan Roth	In this paper, we systematically study the problem of dataless hierarchical text classification.
220	Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization	Alessandro Sordoni, Yoshua Bengio, Jian-Yun Nie	In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases.
221	Fused Feature Representation Discovery for High-Dimensional and Sparse Data	Jun Suzuki, Masaaki Nagata	We formulate our feature representation discovery problem as a variant of the semi-supervised learning problem, namely, as an optimization problem over unsupervised data whose objective is evaluating the impact of each feature with respect to modeling a target task according to the initial model constructed by using supervised data.
222	Instance-Based Domain Adaptation in NLP via In-Target-Domain Logistic Approximation	Rui Xia, Jianfei Yu, Feng Xu, Shumei Wang	In this work, we propose a new instance-based adaptation model, called in-target-domain logistic approximation (ILA).
223	Semi-Supervised Matrix Completion for Cross-Lingual Text Classiﬁcation	Min Xiao, Yuhong Guo	In this work, we propose a novel semi-supervised representation learning approach to address this challenging task by inducing interlingual features via semi-supervised matrix completion.
224	Chinese Overt Pronoun Resolution: A Bilingual Approach	Chen Chen, Vincent Ng	In contrast, we propose a bilingual approach to Chinese pronoun resolution, aiming to improve the resolution of Chinese pronouns by leveraging the publicly available English dictionaries and coreference annotations.
225	Chinese Zero Pronoun Resolution: An Unsupervised Approach Combining Ranking and Integer Linear Programming	Chen Chen, Vincent Ng	To eliminate the reliance on annotated data, we propose an unsupervised approach to this task.
226	Extracting Keyphrases from Research Papers Using Citation Networks	Sujatha Das Gollapalli, Cornelia Caragea	In this work, we study keyphrase extraction from research papers by leveraging citation networks.
227	SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis	PDF –> / 1636	In this paper, we propose a new Supervised User-Item based Topic model, called SUIT model, for sentiment analysis.
228	Lifetime Lexical Variation in Social Media	Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee-Peng Lim	In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages.
229	Detecting Information-Dense Texts in Multiple News Domains	Yinfei Yang, Ani Nenkova	We introduce the task of identifying information-dense texts,which report important factual information in direct, succinct manner.
230	Mind the Gap: Machine Translation by Minimizing the Semantic Gap in Embedding Space	Jiajun Zhang, Shujie Liu, Mu Li, Ming Zhou, Chengqing Zong	In this paper, we propose a Recursive Neural Network (RNN) based model that converts each translation rule into a compact real-valued vector in the semantic embedding space and performs the decoding process by minimizing the semantic gap between the source language string and its translation candidates at each state in a bottom-up structure.
231	Supervised Transfer Sparse Coding	Maruan Al-Shedivat, Jim Jing-Yan Wang, Majed Alzahrani, Jianhua Z. Huang, Xin Gao	In this paper, we explore such possibility and show how a small number of labeled data in the target domain can significantly leverage classification accuracy of the state-of-the-art transfer sparse coding methods.
232	Active Learning with Model Selection	Alnur Ali, Rich Caruana, Ashish Kapoor	We propose an algorithm that actively samples data to simultaneously train a set of candidate models (different model types and/or different hyperparameters) and also select the best model from this set.
233	Multilabel Classification with Label Correlations and Missing Labels	Wei Bi, James T Kwok	Many real-world applications involve multilabel classification, in which the labels can have strong inter-dependencies and some of them may even be missing.Existing multilabel algorithms are unable to handle both issues simultaneously.In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel correlations.By integrating out the missing information, it also provides a disciplinedapproach to the handling of missing labels.
234	Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence	Tim Brys, Ann Nowé, Daniel Kudenko, Matthew E. Taylor	After discussing this problem class, we propose a solution technique for such reinforcement learning problems, called adaptive objective selection.
235	Optimal Neighborhood Preserving Visualization by Maximum Satisfiability	Kerstin Bunte, Matti Järvisalo, Jeremias Berg, Petri Myllymäki, Jaakko Peltonen, Samuel Kaski	We present a novel approach to low-dimensional neighbor embedding for visualization, based on formulating an information retrieval based neighborhood preservation cost function as Maximum satisfiability on a discretized output display.
236	PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences	Róbert Busa-Fekete, Balázs Szörényi, Eyke Hüllermeier	We introduce the problem of PAC rank elicitation, which consists of sorting a given set of options based on adaptive sampling of stochastic pairwise preferences.
237	Manifold Spanning Graphs	CJ Carey, Sridhar Mahadevan	In this paper, the hyperparameter sensitivity of existing graph construction methods is demonstrated.
238	LASS: A Simple Assignment Model with Laplacian Smoothing	Miguel Angel Carreira-Perpinan, Weiran Wang	We propose a simple quadratic programming model that captures this intuition.
239	Distribution-Aware Sampling and Weighted Model Counting for SAT	Supratik Chakraborty, Daniel J. Fremont, Kuldeep S. Meel, Sanjit A. Seshia, Moshe Y. Vardi	Given a CNF formula and a weight for each assignment of values tovariables, two natural problems are weighted model counting anddistribution-aware sampling of satisfying assignments.
240	Dynamic Bayesian Probabilistic Matrix Factorization	Sotirios Chatzis	Motivated by this observation, in this paper we propose a dynamic Bayesian probabilistic matrix factorization model, designed for modeling time-varying distributions.
241	Echo-State Conditional Restricted Boltzmann Machines	Sotirios Chatzis	To resolve these issues, in this paper we propose the echo-state CRBM (ES-CRBM): our model uses an echo-state network reservoir in the context of CRBMs to efficiently capture long and complex temporal dynamics, with much fewertrainable parameters compared to conventional CRBMs.
242	A Local Non-Negative Pursuit Method for Intrinsic Manifold Structure Preservation	Dongdong Chen, Jian Cheng Lv, Zhang Yi	In this paper, a local non-negative pursuit (LNP) method is proposed for neighborhood selection and non-negative representations are learnt.
243	Dropout Training for Support Vector Machines	Ning Chen, Jun Zhu, Jianfei Chen, Bo Zhang	This paper presents dropout training for linear SVMs.
244	Learning with Augmented Class by Exploiting Unlabeled Data	Qing Da, Yang Yu, Zhi-Hua Zhou	In this paper, we tackle the challenge by using unlabeled data, which can be cheaply collected in many real-world applications.
245	Natural Temporal Difference Learning	William Dabney, Philip Thomas	In this paper we investigate the application of natural gradient descent to Bellman error based reinforcement learning algorithms.
246	Learning the Structure of Probabilistic Graphical Models with an Extended Cascading Indian Buffet Process	Patrick Dallaire, Philippe Giguère, Brahim Chaib-draa	This paper presents an extension of the cascading Indian buffet process (CIBP) intended to learning arbitrary directed acyclic graph structures as opposed to the CIBP, which is limited to purely layered structures.
247	Finding Median Point-Set Using Earth Mover’s Distance	Hu Ding, Jinhui Xu	In this paper, we study a prototype learning problem, called Median Point-Set, whose objective is to construct a prototype for a set of given point-sets so as to minimize the total Earth Mover’s Distances (EMD) between the prototype and the point-sets, where EMD between two point-sets is measured under affine transformation.
248	Non-Linear Label Ranking for Large-Scale Prediction of Long-Term User Interests	Nemanja Djuric, Mihajlo Grbovic, Vladan Radosavljevic, Narayan Bhamidipati, Slobodan Vucetic	We propose to address this problem as a task of ranking the ad categories depending on a user’s preference, and introduce a novel label ranking approach capable of efficiently learning non-linear, highly accurate models in large-scale settings.
249	HC-Search for Multi-Label Prediction: An Empirical Study	Janardhan Rao Doppa, Jun Yu, Chao Ma, Alan Fern, Prasad Tadepalli	In this paper, we adapt a recent structured prediction framework called HC-Search for multi-label prediction problems.
250	Learning Instance Concepts from Multiple-Instance Data with Bags as Distributions	Gary Doran, Soumya Ray	We analyze and evaluate a generative process for multiple-instance learning (MIL) in which bags are distributions over instances.
251	Active Learning for Crowdsourcing Using Knowledge Transfer	Meng Fang, Jie Yin, Dacheng Tao	To overcome data scarcity we propose a new probabilistic model that transfers knowledge from abundant unlabeled data in auxiliary domains to help estimate labelers’ expertise.
252	Large-Scale Optimistic Adaptive Submodularity	Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan	In this paper, we propose a scalable learning algorithm for maximizing an adaptive submodular function.
253	Coactive Learning for Locally Optimal Problem Solving	Robby Goetschalckx, Alan Fern, Prasad Tadepalli	In this paper we extend the study of coactive learning to problems where obtaining a globally optimal or near-optimal solution may be intractable or where an expert can only be expected to make small, local improvements to a candidate solution.
254	Kernelized Bayesian Transfer Learning	Mehmet Gönen, Adam A. Margolin	In this paper, we formulate a kernelized Bayesian transfer learning framework that is a principled combination of kernel-based dimensionality reduction models with task-specific projection matrices to find a shared subspace and a coupled classification model for all of the tasks in this subspace.
255	ReLISH: Reliable Label Inference via Smoothness Hypothesis	Chen Gong, Dacheng Tao, Keren Fu, Jie Yang	This paper defines local smoothness, based on which a new algorithm, Reliable Label Inference via Smoothness Hypothesis (ReLISH), is proposed.
256	Signed Laplacian Embedding for Supervised Dimension Reduction	Chen Gong, Dacheng Tao, Jie Yang, Keren Fu	Instead, this paper deploys the signed graph Laplacian and proposes Signed Laplacian Embedding (SLE) for supervised dimension reduction.
257	Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework	Lei Han, Yu Zhang, Guojie Song, Kunqing Xie	In this paper, we propose a probabilistic tree sparsity (PTS) model to utilize the tree structure to obtain the sparse solution instead of the group structure.
258	Deep Modeling of Group Preferences for Group-Based Recommendation	Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Wei Cao	More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines.
259	Fast Multi-Instance Multi-Label Learning	Sheng-Jun Huang, Wei Gao, Zhi-Hua Zhou	Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data.
260	Adaptation-Guided Case Base Maintenance	Vahid Jalali, David Leake	This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases.
261	Intra-View and Inter-View Supervised Correlation Analysis for Multi-View Feature Learning	Xiao-Yuan Jing, Rui-Min Hu, Yang-Ping Zhu, Shan-Shan Wu, Chao Liang, Jing-Yu Yang	In this paper, we mainly study the CCA based multi-view supervised feature learning technique where the labels of training samples are known.
262	Imitation Learning with Demonstrations and Shaping Rewards	Kshitij Judah, Alan Paul Fern, Prasad Tadepalli, Robby Goetschalckx	In this paper, we consider a novel approach to improve the learning efficiency of IL by providing a shaping reward function in addition to the usual demonstrations.
263	Monte Carlo Filtering Using Kernel Embedding of Distributions	Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu	In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings.
264	Power Iterated Color Refinement	Kristian Kersting, Martin Mladenov, Roman Garnett, Martin Grohe	Instead, we treat it as a nonlinear continuous optimization problem and prove thatit implements a conditional gradient optimizer that can be turned into graph clustering approaches using hashing and truncated power iterations.
265	Spectral Thompson Sampling	Tomáš Kocák, Michal Valko, Rémi Munos, Shipra Agrawal	In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the choices are smooth given an underlying graph.
266	Non-Convex Feature Learning via Lp,inf Operator	Deguang Kong, Chris Ding	We present a feature selection method for solving sparse regularization problem, which hasa composite regularization of $\ell_p$ norm and $\ell_{\infty}$ norm.We use proximal gradient method to solve this \L1inf operator problem, where a simple but efficient algorithm is designed to minimize a relatively simple objective function, which contains a vector of $\ell_2$ norm and $\ell_\infty$ norm.
267	Pairwise-Covariance Linear Discriminant Analysis	Deguang Kong, Chris Ding	From this new perspective, we propose a new formulation of LDA, which uses the pairwise averaged class covariance instead of theglobally averaged class covariance used in standard LDA.
268	Constructing Symbolic Representations for High-Level Planning	George Konidaris, Leslie Kaelbling, Tomas Lozano-Perez	We consider the problem of constructing a symbolic description of a continuous, low-level environment for use in planning.
269	Feature-Cost Sensitive Learning with Submodular Trees of Classifiers	Matt Kusner, Wenlin Chen, Quan Zhou, Zhixiang (Eddie) Xu, Kilian Weinberger, Yixin Chen	We propose a different relaxation using approximate submodularity, called Approximately Submodular Tree of Classifiers (ASTC).
270	Scalable Sparse Covariance Estimation via Self-Concordance	Anastasios Kyrillidis, Rabeeh Karimi Mahabadi, Quoc Tran Dinh, Volkan Cevher	We consider the class of convex minimization problems, composed of a self-concordant function, such as the logdet metric, a convex data fidelity term h(.)
271	Wormhole Hamiltonian Monte Carlo	Shiwei Lan, Jeffrey Streets, Babak Shahbaba	To address this issue, we propose a novel Bayesian inference approach based on Markov Chain Monte Carlo.
272	Manifold Learning for Jointly Modeling Topic and Visualization	Tuan M. V. Le, Hady W. Lauw	We therefore propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces.
273	Partial Multi-View Clustering	Shao-Yuan Li, Yuan Jiang, Zhi-Hua Zhou	In this paper,we present possibly the first study on partial multiviewclustering.
274	Sample-adaptive Multiple Kernel Learning	Xinwang Liu, Lei Wang, Jian Zhang, Jianping Yin	To improve this situation, we propose a sample-adaptive MKL algorithm, in which base kernels are allowed to be adaptively switched on/off with respect to each sample.
275	Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network	Yong Luo, Jian Tang, Jun Yan, Chao Xu, Zheng Chen	In this paper, we proposed a two-side multimodal neural network to learn a robust word embedding from multiple data sources including free text, user search queries and search click-through data.
276	Convex Co-embedding	Farzaneh Mirzazadeh, Yuhong Guo, Dale Schuurmans	We present a general framework for association learning, where entities are embedded in a common latent space to express relatedness by geometry — an approach that underlies the state of the art for link prediction, relation learning, multi-label tagging, relevance retrieval and ranking.
277	Mixing-Time Regularized Policy Gradient	Tetsuro Morimura, Takayuki Osogami, Tomoyuki Shirai	In particular, we propose a method of temporal-difference learning for estimating the gradient of the hitting time.
278	Semantic Data Representation for Improving Tensor Factorization	Makoto Nakatsuji, Yasuhiro Fujiwara, Hiroyuki Toda, Hiroshi Sawada, Jin Zheng, James Alexander Hendler	Semantic Data Representation for Improving Tensor Factorization
279	Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning	Cam-Tu Nguyen, Xiaoliang Wang, Jing Liu, Zhi-Hua Zhou	To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference.
280	Online and Stochastic Learning with a Human Cognitive Bias	Hidekazu Oiwa, Hiroshi Nakagawa	Our main contributions are 1) to experimentally show its effect for user utilities as a human cognitive bias, 2) to formalize a new framework by internalizing this bias into the optimization problem, 3) to develop new algorithms without memorization of the past prediction history, and 4) to show some theoretical guarantees of our derived algorithm for both online and stochastic learning settings.
281	Robust Non-Negative Dictionary Learning	Qihe Pan, Deguang Kong, Chris Ding, Bin Luo	In this paper, we propose a new formulation for non-negative dictionary learning in noisy environment, where structure sparsity is enforced on sparse representation.
282	Evolutionary Dynamics of Q-Learning over the Sequence Form	Fabio Panozzo, Nicola Gatti, Marcello Restelli	We show that, although sequence-form and normal-form replicator dynamics are realization equivalent, the Q-learning algorithm applied to the two forms have non-realization equivalent dynamics.
283	Bagging by Design (on the Suboptimality of Bagging)	Periklis Papakonstantinou, Jia Xu, Zhu Cao	Most importantly, we provide an alternative subsampling method called design-bagging based on a new construction of combinatorial designs, and prove it universally better than bagging.
284	Anytime Active Learning	Maria Eugenia Ramirez-Loaiza, Aron Culotta, Mustafa Bilgic	In this paper, we investigate whether we can train learning systems more efficiently by requesting an annotation before inspection is fully complete — e.g., after reading only 25 words of a document.
285	On Boosting Sparse Parities	Lev Reyzin	In this paper we consider the problem of designing weak learners thatare especially adept to the boosting procedure and specifically the AdaBoost algorithm.
286	Online Multi-Task Learning via Sparse Dictionary Optimization	Paul Ruvolo, Eric Eaton	This paper develops an efficient online algorithm for learning multiple consecutive tasks based on the K-SVD algorithm for sparse dictionary optimization.
287	A Hybrid Grammar-Based Approach for Learning and Recognizing Natural Hand Gestures	Amir Sadeghipour, Stefan Kopp	In this paper, we present a hybrid grammar formalism designed to learn structured models of natural iconic gesture performances that allow for compressed representation and robust recognition.
288	Sparse Compositional Metric Learning	Yuan Shi, Aurélien Bellet, Fei Sha	We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data.
289	Locality Preserving Projection for Domain Adaptation with Multi-Objective Learning	Le Shu, Tianyang Ma, Longin Jan Latecki	In many practical cases, we need to generalize a model trained in a source domain to a new target domain.However, the distribution of these two domains may differ very significantly, especially sometimes some crucial target features may not have support in the source domain.This paper proposes a novel locality preserving projection method for domain adaptation task,which can find a linear mapping preserving the ‘intrinsic structure’ for both source and target domains.We first construct two graphs encoding the neighborhood information for source and target domains separately.We then find linear projection coefficients which have the property of locality preserving for each graph.Instead of combing the two objective terms under compatibility assumption and requiring the user to decide the importance of each objective function,we propose a multi-objective formulation for this problem and solve it simultaneously using Pareto optimization.The Pareto frontier captures all possible good linear projection coefficients that are preferred by one or more objectives.The effectiveness of our approach is justified by both theoretical analysis and empirical results on real world data sets.The new feature representation shows better prediction accuracy as our experiments demonstrate.
290	Reconsidering Mutual Information Based Feature Selection: A Statistical Significance View	Nguyen Xuan Vinh, Jeffrey Chan, James Bailey	In this paper, we argue a different viewpoint that, given a very large amount of data, the high dimensional MI objective is still problematic to be employed as a meaningful optimization criterion, due to its overfitting nature: the MI almost always increases as more features are added, thus leading to a trivial solution which includes all features.
291	Cross-Domain Metric Learning Based on Information Theory	Hao Wang, Wei Wang, Chen Zhang, Fanjiang Xu	In this paper, we propose a novel metric learning algorithm to transfer knowledge from the source domain to the target domain in an information-theoretic setting, where a shared Mahalanobis distance across two domains is learnt by combining three goals together: 1) reducing the distribution difference between different domains; 2) preserving the geometry of target domain data; 3) aligning the geometry of source domain data with its label information.
292	Improving Semi-Supervised Target Alignment via Label-Aware Base Kernels	Qiaojun Wang, Kai Zhang, Guofei Jiang, Ivan Maric	In this paper, we propose to construct high-quality base kernels with the help of label information to globally improve the final target alignment.
293	Exact Subspace Clustering in Linear Time	Shusen Wang, Bojun Tu, Congfu Xu, Zhihua Zhang	In this paper we exploit a data selection algorithm to speedup computation and the robust principal component analysis to strengthen robustness.
294	Using The Matrix Ridge Approximation to Speedup Determinantal Point Processes Sampling Algorithms	Shusen Wang, Chao Zhang, Hui Qian, Zhihua Zhang	In this paper we employ the matrix ridge approximation (MRA) to speedup the sampling algorithm of DPP, showing that our approach MRA-DPP has stronger error bound than the Nystrom-DPP.
295	The Role of Dimensionality Reduction in Classification	Weiran Wang, Miguel Angel Carreira-Perpinan	Best performance would be obtained by optimizing the classification error jointly over DR mapping and classifier (a wrapper approach), but this is a difficult nonconvex problem, particularly with nonlinear DR. Using the method of auxiliary coordinates, we give a simple, efficient algorithm to train a combination of nonlinear DR and a classifier, and apply it to a RBF mapping with a linear SVM.
296	Small-Variance Asymptotics for Dirichlet Process Mixtures of SVMs	Yining Wang, Jun Zhu	This paper presents a small-variance asymptotic analysis to derive a simple and efficient algorithm, which monotonically optimizes a max-margin DP-means (M2DPM) problem, an extension of DP-means for both predictive learning and descriptive clustering.
297	Learning Relative Similarity by Stochastic Dual Coordinate Ascent	Pengcheng Wu, Yi Ding, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi	In this paper, we investigate the application of Stochastic Dual Coordinate Ascent (SDCA) technique to tackle the optimization task of relative similarity learning by extending from vector to matrix parameters.
298	Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition	Rongkai Xia, Yan Pan, Lei Du, Jian Yin	In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC).
299	Supervised Hashing for Image Retrieval via Image Representation Learning	Rongkai Xia, Yan Pan, Hanjiang Lai, Cong Liu, Shuicheng Yan	In this paper, we propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as well as a set of hash functions.
300	Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer’s Disease	Bo Xin, Yoshinobu Kawahara, Yizhou Wang, Wen Gao	In this study, we propose a fast and scalable algorithm for GFL.
301	Online Classification Using a Voted RDA Method	Tianbing Xu, Jianfeng Gao, Lin Xiao, Amelia C. Regan	We propose a voted dual averaging method for on- line classification problems with explicit regularization.
302	Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization	Dongqing Zhang, Wu-Jun Li	In this paper, a novel SMH method, called semantic correlation maximization~(SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling.
303	Multi-Instance Learning with Distribution Change	Wei-Jia Zhang, Zhi-Hua Zhou	We propose the MICS approach by considering both bag-level and instance-level distribution change.
304	Novel Density-Based Clustering Algorithms for Uncertain Data	Xianchao Zhang, Han Liu, Xiaotong Zhang, Xinyue Liu	Novel Density-Based Clustering Algorithms for Uncertain Data
305	Robust Bayesian Inverse Reinforcement Learning with Sparse Behavior Noise	Jiangchuan Zheng, Siyuan Liu, Lionel M. Ni	To model such noise, we introduce a novel latent variable characterizing the reliability of each expert action and use Laplace distribution as its prior.
306	Gradient Descent with Proximal Average for Nonconvex and Composite Regularization	Wenliang Zhong, James T. Kwok	In thispaper, byusing a recent mathematical tool known as the proximal average,we propose a novel proximal gradient descent method for optimization with a wide class of nonconvex and composite regularizers.Instead of directlysolving the proximal stepassociated with a composite regularizer, we average thesolutions from the proximal problems of the constituent regularizers.
307	Hybrid Heterogeneous Transfer Learning through Deep Learning	Joey Tianyi Zhou, Sinno Jialin Pan, Ivor W. Tsang, Yan Yan	In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain.
308	Oversubscription Planning: Complexity and Compilability	Meysam Aghighi, Peter Jonsson	We present complexity results for the so-called partial satisfaction and net benefit problems under various restrictions; this extends previous work by van den Briel et al.
309	Planning as Model Checking in Hybrid Domains	Sergiy Bogomolov, Daniele Magazzeni, Andreas Podelski, Martin Wehrle	In this paper, we make a first step in bridging the gap between these two worlds.
310	Flexible and Scalable Partially Observable Planning with Linear Translations	Blai Bonet, Hector Geffner	In this work, we combine the benefits of the two approaches – the scope of the CLG planner and the efficiency of the Kreplanner.
311	Using Timed Game Automata to Synthesize Execution Strategies for Simple Temporal Networks with Uncertainty	Alessandro Cimatti, Luke Hunsberger, Andrea Micheli, Marco Roveri	This paper provides a novel mapping from STNUs to Timed Game Automata (TGAs) that: (1) explicates the deep theoretical relationships between STNUs and TGAs; and (2) enables the memoryless strategies generated from the TGA to be transformed into equivalent STNU execution strategies that reduce the real-time computational burden for the executor.
312	Scheduling for Transfers in Pickup and Delivery Problems with Very Large Neighborhood Search	Brian Coltin, Manuela Veloso	We introduce the Very Large Neighborhood Search with Transfers (VLNS-T) algorithm to form schedules for the PDP-T.
313	Structured Possibilistic Planning Using Decision Diagrams	Nicolas Drougard, Florent Teichteil-Königsbuch, Jean-Loup Farges, Didier Dubois	In this paper, we propose the first study of factored pi-MOMDP models in order to solve large structured planning problems under qualitative uncertainty, or considered as qualitative approximations of probabilistic problems.
314	Chance-Constrained Probabilistic Simple Temporal Problems	Cheng Fang, Peng Yu, Brian C. Williams	In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing.
315	Solving the Traveling Tournament Problem by Packing Three-Vertex Paths	Marc Goerigk, Richard Hoshino, Ken-ichi Kawarabayashi, Stephan Westphal	In this paper, we tackle the TTP from a graph-theoretic perspective, by generating a new “canonical” schedule in which each team’s three-game road trips match up with the underlying graph’s minimum-weight P_3-packing.
316	Delivering Guaranteed Display Ads under Reach and Frequency Requirements	Ali Hojjat, John Turner, Suleyman Cetintas, Jian Yang	We propose a novel idea in the allocation and serving of online advertising.
317	Solving Uncertain MDPs by Reusing State Information and Plans	Ping Hou, William Yeoh, Tran Cao Son	In this paper, we introduce a general framework that allows off-the-shelf MDP algorithms to solve Uncertain MDPs by planning based on currently available information and replan if and when the problem changes.
318	Grandpa Hates Robots – Interaction Constraints for Planning in Inhabited Environments	Uwe Koeckemann, Federico Pecora, Lars Karlsson	In this paper we introduce an approach for automatically generating plans that are conformant wrt. given ICs and partially specified human activities.
319	Backdoors to Planning	Martin Kronegger, Sebastian Ordyniak, Andreas Pfandler	In this work, we introduce two notions of backdoors building upon the causal graph.
320	A Simple Polynomial-Time Randomized Distributed Algorithm for Connected Row Convex Constraints	T. K. Satish Kumar, Duc Thien Nguyen, William Yeoh, Sven Koenig	In this paper, we describe a simple randomized algorithm that runs in polynomial time and solves connected row convex (CRC) constraints in distributed settings.
321	Symbolic Domain Predictive Control	Johannes Löhr, Martin Wehrle, Maria Fox, Bernhard Nebel	In this paper, we extend the approach to deal with symbolic states.
322	Computing Contingent Plans via Fully Observable Non-Deterministic Planning	Christian Muise, Vaishak Belle, Sheila A. McIlraith	Here we push the envelope on this challenging problem, proposing a technique for generating conditional (aka contingent) plans offline.
323	A Scheduler for Actions with Iterated Durations	James G Paterson, Eric Timmons, Brian C Williams	In this paper, we introduce the Looping Temporal Problem with Preference (LTPP) as a simple parameterized extension of a simple temporal problem.
324	Parametrized Families of Hard Planning Problems from Phase Transitions	Eleanor Rieffel, Davide Venturelli, Minh Do, Itay Hen, Jeremy Frank	There are two complementary ways to evaluate planning algorithms: performance on benchmark problems derived from real applications and analysis of performance on parametrized families of problems with known properties. We generate hard planning problems from the solvable/unsolvable phase transition region of well-studied NP-complete problems that map naturally to navigation and scheduling, aspects common to many planning domains.
325	Cost-Based Query Optimization via AI Planning	Nathan Robinson, Sheila McIlraith, David Toman	In this paper we revisit the problem of generating query plans using AI automated planning with a view to leveraging significant recent advances in state-of-the-art planning techniques.
326	Efficiently Implementing GOLOG with Answer Set Programming	Malcolm Ryan	In this paper we investigate three different approaches to encoding domain-dependent control knowledge for Answer-Set Planning.
327	Generalized Label Reduction for Merge-and-Shrink Heuristics	Silvan Sievers, Martin Wehrle, Malte Helmert	We generalize this theory so that labels can be reduced in every intermediate abstraction of a merge-and-shrink tree.
328	Saturated Path-Constrained MDP: Planning under Uncertainty and Deterministic Model-Checking Constraints	Jonathan Sprauel, Andrey Kolobov, Florent Teichteil-Königsbuch	We propose a dynamic programming-based algorithm for finding such policies, and empirically demonstrate this algorithm to be orders of magnitude faster than its next-best alternative.
329	A Relevance-Based Compilation Method for Conformant Probabilistic Planning	Ran Taig, Ronen I. Brafman	Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus.
330	Optimal Decoupling in Linear Constraint Systems	Cees Witteveen, Michel Wilson, Tomas Klos	In this paper, we concentrate on linear constraint systems and efficient decomposition techniques for them.
331	Adding Local Exploration to Greedy Best-First Search in Satisficing Planning	Fan Xie, Martin Müller, Robert Holte	This work analyzes the problem of UHRs in planning in detail, and proposes a two level search framework as a solution.
332	Type-Based Exploration with Multiple Search Queues for Satisficing Planning	Fan Xie, Martin Müller, Robert Holte, Tatsuya Imai	The current work introduces a search algorithm that utilizes type systems in a new way – for exploration within a GBFS multiqueue framework in satisficing planning.
333	Lifting Relational MAP-LPs Using Cluster Signatures	Udi Apsel, Kristian Kersting, Martin Mladenov	In this paper, we improve upon efficiency in two ways.
334	Recovering from Selection Bias in Causal and Statistical Inference	Elias Bareinboim, Jin Tian, Judea Pearl	In this paper, we provide complete graphical and algorithmic conditions for recovering conditional probabilities from selection biased data.
335	Tree-Based On-Line Reinforcement Learning	Andre M. S. Barreto	This paper aims to address this specific issue.
336	Testable Implications of Linear Structural Equation Models	Bryant Chen, Jin Tian, Judea Pearl	In this paper, we extend the half-trek criterion of (Foygel et al., 2012) to identify a larger set of structural coefficients and use it to systematically discover overidentifying constraints.
337	Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging	Yetian Chen, Jin Tian	In this paper we develop an algorithm to find the k-best equivalence classes of Bayesian networks.
338	Tightening Bounds for Bayesian Network Structure Learning	Xiannian Fan, Changhe Yuan, Brandon Malone	Tightening Bounds for Bayesian Network Structure Learning
339	State Aggregation in Monte Carlo Tree Search	Jesse Hostetler, Alan Fern, Tom Dietterich	In this paper, we study state aggregation as a way of reducing stochastic branching in tree search.
340	Relational One-Class Classification: A Non-Parametric Approach	Tushar Khot, Sriraam Natarajan, Jude Shavlik	We propose a non-parametric relational one-class classification approach based on first-order trees.
341	Predicting the Hardness of Learning Bayesian Networks	Brandon Malone, Kustaa Kangas, Matti Jarvisalo, Mikko Koivisto, Petri Myllymaki	The main contribution of this paper is characterization of the empirical hardness of an instance for a given algorithm based on a novel collection of non-trivial, yet efficiently computable features.
342	Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference	Mathias Niepert, Guy Van den Broeck	We develop a theory of finite exchangeability and its relation to tractable probabilistic inference.
343	R2: An Efficient MCMC Sampler for Probabilistic Programs	Aditya Nori, Chung-Kil Hur, Sriram Rajamani, Selva Samuel	We present a new Markov Chain Monte Carlo (MCMC) sampling algorithm for probabilistic programs.
344	An Adversarial Interpretation of Information-Theoretic Bounded Rationality	Pedro A. Ortega, Daniel D. Lee	Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary.
345	Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics	Joris Renkens, Angelika Kimmig, Guy Van den Broeck, Luc De Raedt	We contribute a new bounded approximation method for weighted model counting based on probabilistic logic programming principles.
346	Approximate Lifting Techniques for Belief Propagation	Parag Singla, Aniruddh Nath, Pedro M. Domingos	To overcome these problems, we present approximate lifted inference, which groups together similar but distinguishable objects and treats them as if they were identical.
347	Decentralized Stochastic Planning with Anonymity in Interactions	Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet	In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction.
348	Point-Based POMDP Solving with Factored Value Function Approximation	Tiago Veiga, Matthijs Spaan, Pedro Lima	In particular, we present a backup operator that can be used in any point-based POMDP solver.
349	Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects	Abdeslam Boularias, James Andrew Bagnell, Anthony Stentz	To reduce the computational burden of this last operation, we model the outcomes of the grasps as a Gaussian Process, and use an entropy-search method in order to focus the optimization on regions where the best grasp is most likely to be.
350	A Framework for Task Planning in Heterogeneous Multi Robot Systems Based on Robot Capabilities	Jennifer Buehler, Maurice Pagnucco	We propose a platform-independent model of robot capabilities which we use as a planning domain.
351	Generalizing Policy Advice with Gaussian Process Bandits for Dynamic Skill Improvement	Jared Glover, Charlotte Zhu	We present a ping-pong-playing robot that learns to improve its swings with human advice.
352	Minimising Undesired Task Costs in Multi-Robot Task Allocation Problems with In-Schedule Dependencies	Bradford Heap, Maurice Pagnucco	We reduce this influence by calculating a novel task cost dispersion value that measures robots’ collective preference for each task.
353	Optimal and Efficient Stochastic Motion Planning in Partially-Known Environments	Ryan J Luna, Morteza Lahijanian, Mark Moll, Lydia E Kavraki	A framework capable of computing optimal control policies for a continuous system in the presence of both action and environment uncertainty is presented in this work.
354	Learning from Unscripted Deictic Gesture and Language for Human-Robot Interactions	Cynthia Matuszek, Liefeng Bo, Luke Zettlemoyer, Dieter Fox	In this work, we investigate how people refer to objects in the world during relatively unstructured communication with robots. We collect a corpus of deictic interactions from users describing objects, which we use to train language and gesture models that allow our robot to determine what objects are being indicated.
355	Robust Visual Robot Localization Across Seasons Using Network Flows	Tayyab Naseer, Luciano Spinello, Wolfram Burgard, Cyrill Stachniss	In this paper, we present a novel approach to visual localization of mobile robots in outdoor environments, which is able to deal with substantial seasonal changes.
356	Schedule-Based Robotic Search for Multiple Residents in a Retirement Home Environment	Markus Sebastian Schwenk, Tiago Stegun Vaquero, Goldie Nejat, Kai O. Arras	In this paper we address the planning problem of a robot searching for multiple residents in a retirement home in order to remind them of an upcoming multi-person recreational activity before a given deadline.
357	Qualitative Planning with Quantitative Constraints for Online Learning of Robotic Behaviours	Timothy Wiley, Claude Sammut, Ivan Bratko	This paper resolves previous problems in the Multi-Strategy architecture for online learning of robotic behaviours.
358	GP-Localize: Persistent Mobile Robot Localization Using Online Sparse Gaussian Process Observation Model	Nuo Xu, Kian Hsiang Low, Jie Chen, Keng Kiat Lim, Etkin Baris Ozgul	This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurements taken during a robot’s exploration (instead of relying on prior training data) for efficiently and scalably learning the GP observation model online through our proposed novel online sparse GP.
359	MaxSAT by Improved Instance-Specific Algorithm Configuration	Carlos Ansotegui, Yuri Malitsky, Meinolf Sellmann	Our objective is to boost the state-of-the-art performance in MaxSATsolving.
360	Adaptive Singleton-Based Consistencies	Amine Balafrej, Christian Bessiere, El Houssine Bouyakhf, Gilles Trombettoni	In this paper, we focus on partition-one-AC, a singleton-based consistency which, as opposed to singleton arc consistency, is able to prune values on all variables when it performs singleton tests on one of them.
361	Non-Restarting SAT Solvers with Simple Preprocessing Can Efficiently Simulate Resolution	Paul Beame, Ashish Sabharwal	The precise class of formulas for which they can produce polynomial size refutations has been the subject of several studies, with special focus on the clause learning aspect of these solvers.
362	Propagating Regular Counting Constraints	Nicolas Beldiceanu, Pierre Flener, Justin Pearson, Pascal Van Hentenryck	We consider counter-DFAs (cDFA), which provide concise models for regular counting constraints, that is constraints over the number of times a regular-language pattern occurs in a sequence.
363	Tailoring Local Search for Partial MaxSAT	Shaowei Cai, Chuan Luo, John Thornton, Kaile Su	In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses.
364	Q-Intersection Algorithms for Constraint-Based Robust Parameter Estimation	Clement Carbonnel, Gilles Trombettoni, Philippe Vismara, Gilles Chabert	We present a computational study of the q-intersection.
365	Linear-Time Filtering Algorithms for the Disjunctive Constraint	Hamed Fahimi, Claude-Guy Quimper	We present three new filtering algorithms for the Disjunctive constraint that all have a linear running time complexity in the number of tasks.
366	Diagnosing Analogue Linear Systems Using Dynamic Topological Reconfiguration	Alexander Feldman, Gregory Provan	We study a novel algorithm that addresses both problems.
367	Backdoors into Heterogeneous Classes of SAT and CSP	Serge Gaspers, Neeldhara Misra, Sebastian Ordyniak, Stefan Szeider, Stanislav Zivny	By instantiating the backdoor variables one reduces the given instance to several easy instances that belong to a tractable class.The overall time needed to solve the instance is exponential in the size of the backdoor set, hence it is a challenging problem to find a small backdoor set if one exists; over the last years this problem has been subject of intensive research.
368	A Reasoner for the RCC-5 and RCC-8 Calculi Extended with Constants	Stella Giannakopoulou, Charalampos Nikolaou, Manolis Koubarakis	In this paper we present the first reasoner that takes as input RCC-5 or RCC-8 networks with variables and constants and decides their consistency.
369	An Experimentally Efficient Method for (MSS,CoMSS) Partitioning	Eric Grégoire, Jean-Marie Lagniez, Bertrand Mazure	Inthis paper, a novel algorithm for partitioning a BooleanCNF formula into one MSS and the correspondingCoMSS is introduced.
370	A Support-Based Algorithm for the Bi-Objective Pareto Constraint	Renaud Hartert, Pierre Schaus	This paper introduces a simpler and more efficient filtering algorithm for the bi-objective Pareto constraint.
371	DJAO: A Communication-Constrained DCOP Algorithm that Combines Features of ADOPT and Action-GDL	Yoonheui Kim, Victor Lesser	In this paper we propose a novel DCOP algorithm, called DJAO, that is able toefficiently find a solution with low communication overhead; this algorithm can be used for optimal and bounded approximate solutions by appropriately setting the error bounds.
372	Preprocessing for Propositional Model Counting	Jean-Marie Lagniez, Pierre Marquis	This paper is concerned with preprocessing techniques for propositional model counting.
373	Boosting SBDS for Partial Symmetry Breaking in Constraint Programming	Jimmy H.M. Lee, Zichen Zhu	The paper proposes a dynamic method, Recursive SBDS(ReSBDS), for efficient partial symmetry breaking.
374	Double Configuration Checking in Stochastic Local Search for Satisfiability	Chuan Luo, Shaowei Cai, Wei Wu, Kaile Su	In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way.
375	A Propagator Design Framework for Constraints over Sequences	Jean-Noel Monette, Pierre Flener, Justin Pearson	We propose a conceptual framework for designing such propagators: pruning rules, in a functional notation, are refined upon the application of transformation operators to a DP-style formulation of a constraint; a representation of the (tuple) variable domains is picked; and a control of the pruning rules is picked.
376	Maximum Satisfiability Using Core-Guided MaxSAT Resolution	Nina Narodytska, Fahiem Bacchus	In this work we propose an alternative approach.
377	Fast Consistency Checking of Very Large Real-World RCC-8 Constraint Networks Using Graph Partitioning	Charalampos Nikolaou, Manolis Koubarakis	We present a new reasoner for RCC-8 constraint networks, called gp-rcc8, that is based on the patchwork property of path-consistent tractable RCC-8 networks and graph partitioning.
378	Avoiding Plagiarism in Markov Sequence Generation	Alexandre Papadopoulos, Pierre Roy, François Pachet	In the framework of constraint satisfaction (CSP), we introduce MaxOrder.
379	Cached Iterative Weakening for Optimal Multi-Way Number Partitioning	Ethan L Schreiber, Richard E Korf	We present a new algorithm, cached iterative weakening (CIW), for solving this problem optimally.
380	Exploiting Competition Relationship for Robust Visual Recognition	Liang Du, Haibin Ling	With the help of auxiliary competing tasks, we can identify such features within a joint learning model exploiting the competition relationship.Motivated by this intuition, we propose a novel way to exploit competition relationship for solving visual recognition problems.
381	Towards Topological-Transformation Robust Shape Comparison: A Sparse Representation Based Manifold Embedding Approach	Longwen Gao, Shuigeng Zhou	In other words, it is sensitive totopological transformations such as stretching and compressing.To tackle this problem, we propose a new approachthat constructs a high-dimensional space to embedthe manifolds of shapes based on sparse representation,which is able to completely withstand rigid transformationsand considerably tolerate topological transformations.Experiments on TOSCA shapes validate theproposed approach.
382	Grounding Acoustic Echoes in Single View Geometry Estimation	Muhammad Wajahat Hussain, Javier Civera, Luis Montano	In this work we add the physical constraints coming from acoustic echoes, generated by an audio source, to this visual model.
383	Similarity-Preserving Binary Signature for Linear Subspaces	Jianqiu Ji, Jianmin Li, Shuicheng Yan, Qi Tian, Bo Zhang	We provide a lower bound on the length of the binary signatures which suffices to guarantee uniform distance-preservation within a set of subspaces.
384	Deep Salience: Visual Salience Modeling via Deep Belief Propagation	Richard Jiang, Danny Crookes	In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation.
385	Locality-Constrained Low-Rank Coding for Image Classification	Ziheng Jiang, Ping Guo, Lihong Peng	To solve this problem, we propose a locality-constrained low-rank coding (LCLR) algorithm for image representations.
386	Uncorrelated Multi-View Discrimination Dictionary Learning for Recognition	Xiao-Yuan Jing, Rui-Min Hu, Fei Wu, Xi-Lin Chen, Qian Liu, Yong-Fang Yao	To boost the performance of multi-view DL technique, we propose an uncorrelated multi-view discrimination DL (UMDDL) approach for recognition.
387	Learning to Recognize Novel Objects in One Shot through Human-Robot Interactions in Natural Language Dialogues	Evan A. Krause, Michael Zillich, Thomas Williams, Matthias Scheutz	In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object.
388	Sub-Selective Quantization for Large-Scale Image Search	Yeqing Li, Chen Chen, Wei Liu, Junzhou Huang	To address this issue, we propose a sub-selection based matrix manipulation algorithm which can significantly reduce the computational cost of code learning.
389	Learning Low-Rank Representations with Classwise Block-Diagonal Structure for Robust Face Recognition	Yong Li, Jing Liu, Zechao Li, Yangmuzi Zhang, Hanqing Lu, Songde Ma	Motivated by the success of low-rank matrix recovery, we propose a novel semi-supervised low-rank matrix recovery algorithm for robust face recognition.
390	Efficient Object Detection via Adaptive Online Selection of Sensor-Array Elements	Matthai Philipose	We pose the ensemble sensor selection problem as a structured extension of test-cost-sensitive classification, propose a principled suite of techniques to exploit ensemble structure to speed up processing and show how to re-estimate policies fast.
391	On Hair Recognition in the Wild by Machine	Joseph Roth, Xiaoming Liu	We present an algorithm for identity verification using only information from the hair.
392	Diagram Understanding in Geometry Questions	Min Joon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni	In this paper, we present a method for diagram understanding that identifies visual elements in a diagram while maximizing agreement between textual and visual data.
393	A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types	Dror Sholomon, Omid E. David, Nathan S. Netanyahu	In this paper we introduce new types of square-piece jigsaw puzzles, where in addition to the unknown location and orientation of each piece, a piece might also need to be flipped. This paper also presents, among other results, the most extensive set of experimental results, compiled as of yet, on Type 2 puzzles.
394	Low-Rank Tensor Completion with Spatio-Temporal Consistency	Hua Wang, Feiping Nie, Heng Huang	To solve this problem, we propose a novel spatially-temporally consistent tensor completion method for recovering the video missing data.
395	Semantic Graph Construction for Weakly-Supervised Image Parsing	Wenxuan Xie, Yuxin Peng, Jianguo Xiao	In order to embed more semantics into the affinity graph, we propose novel criteria by exploiting the weak supervision information carefully, and develop two graphs: L1 semantic graph and k-NN semantic graph.
396	Latent Domains Modeling for Visual Domain Adaptation	Caiming Xiong, Scott McCloskey, Shao-Hang Hsieh, Jason J. Corso	In this paper, we propose a model that automatically discovers latent domains in visual datasets.
397	Semantic Segmentation Using Multiple Graphs with Block-Diagonal Constraints	Ke Zhang, Wei Zhang, Sheng Zeng, Xiangyang Xue	In this paper we propose a novel method for image semantic segmentation using multiple graphs.
398	Locality Preserving Hashing	Kang Zhao, Hongtao Lu, Jincheng Mei	In this paper, we propose a novel hashing algorithm called Locality Preserving Hashing to effectively solve the above problems.
399	Predictive Models for Determining If and When to Display Online Lead Forms	Tim Chan, Joseph I, Carlos Macasaet, Daniel Kang, Robert M. Hardy, Carlos Ruiz, Rigel Porras, Brian Baron, Karim Qazi, Padraic Hannon, Tomonori Honda	This paper will demonstrate a machine learning appli- cation for predicting positive lead conversion events on the Edmunds.com website, an American destination for car shopping.
400	Engineering Works Scheduling for Hong Kong’s Rail Network	Andy Hon Wai Chun, Ted Yiu Tat Suen	This paper describes how AI is used to plan, schedule, and optimize nightly engineering works for both the commuter and rapid transit lines in Hong Kong.
401	THink: Inferring Cognitive Status from Subtle Behaviors	Randall Davis, David J. Libon, Rhoda Au, David Pitman, Dana L Penney	We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years.
402	The Quest Draft: An Automated Course Allocation Algorithm	Richard Hoshino, Caleb Raible-Clark	In this paper, we present a four-part AI-based course allocation algorithm that was conceived by an undergraduate student, and recently implemented at a small Canadian liberal arts university.
403	Evaluation and Deployment of a People-to-People Recommender in Online Dating	Alfred Krzywicki, Wayne Wobcke, Yang Sok Kim, Xiongcai Cai, Michael Bain, Paul Compton, Ashesh Mahidadia	This paper reports on the successful deployment of a people-to-people recommender system in a large commercial online dating site.
404	Deploying CommunityCommands: A Software Command Recommender System Case Study	Wei Li, Justin Matejka, Tovi Grossman, George Fitzmaurice	In this paper, we present our system usage data and payoff.
405	CiteSeerX: AI in a Digital Library Search Engine	Jian Wu, Kyle Williams, Hung-Hsuan Chen, Madian Khabsa, Cornelia Caragea, Alexander Ororbia, Douglas Jordan, C. Lee Giles	We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation.
406	Pattern Discovery in Protein Networks Reveals High-Confidence Predictions of Novel Interactions	Hazem Radwan Ahmed, Janice I. Glasgow	This paper proposes a computational framework to efficiently discover topologically-similar patterns from large proteomic networks using Particle Swarm Optimization (PSO).
407	Crowdsourcing for Multiple-Choice Question Answering	Bahadir Ismail Aydin, Yavuz Selim Yilmaz, Yaliang Li, Qi Li, Jing Gao, Murat Demirbas	We leverage crowd wisdom for multiple-choice question answering, and employ lightweight machine learning techniques to improve the aggregation accuracy of crowdsourced answers to these questions.
408	Advice Provision for Energy Saving in Automobile Climate Control Systems	Amos Azaria, Sarit Kraus, Claudia V. Goldman, Omer Tsimhoni	In this paper we consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit.
409	A Smart Range Helping Cognitively-Impaired Persons Cooking	Bruno Bouchard, Kevin Bouchard, Abdenour Bouzouane	Therefore, we present in this paper a new smart range prototype allowing monitoring and guiding a cognitively-impaired user in the activity of preparing a meal.
410	STREETS: Game-Theoretic Traffic Patrolling with Exploration and Exploitation	Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe	In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defender-attacker Stackelberg game.
411	Swissnoise: Online Polls with Game-Theoretic Incentives	Florent Garcin, Boi Faltings	In this paper, we describe an experimental platform, swissnoise, that compares prediction markets with peer prediction schemes developed in recent AI research.
412	Robust Protection of Fisheries with COmPASS	William Haskell, Debarun Kar, Fei Fang, Milind Tambe, Sam Cheung, Elizabeth Denicola	We have developed the COmPASS (Conserva- tive Online Patrol ASSistant) system to design USCG patrols against the Lanchas.
413	Optimizing a Start-Stop Controller Using Policy Search	Noel Hollingsworth, Jason Meyer, Ryan McGee, Jeffrey Doering, George Konidaris, Leslie Kaelbling	We applied a policy search algorithm to the problem of optimizing a start-stop controller — a controller used in a car to turn off the vehicle’s engine, and thus save energy, when the vehicle comes to a temporary halt.
414	A Unified Framework for Augmented Reality and Knowledge-Based Systems in Maintaining Aircraft	Geun-Sik Jo, Kyeong-Jin Oh, Inay Ha, Kee-Sung Lee, Myung-Duk Hong, Ulrich Neumann, Suya You	This paper proposes intelligent augmented reality (IAR) system to minimize operation errors and time-related costs and help aircraft technicians cope with complex tasks by using an intuitive UI/UX interface for their maintenance tasks.
415	A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests	Jeremy Ludwig, Annaka Kalton, Robert Richards, Brian Bautsch, Craig Markusic, J. Schumacher	The work presented in this paper describes how an existing intelligent scheduling software framework was modified to include domain-specific heuristics used in the vehicle test planning process.
416	AI-MIX: Using Automated Planning to Steer Human Workers Towards Better Crowdsourced Plans	Lydia Manikonda, Tathagata Chakraborti, Sushovan De, Kartik Talamadupula, Subbarao Kambhampati	In this paper, we argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering the crowd.
417	A Speech-Driven Second Screen Application for TV Program Discovery	Peter Z. Yeh, Ben Douglas, William Jarrold, Adwait Ratnaparkhi, Deepak Ramachandran, Peter F. Patel-Schneider, Stephen Laverty, Nirvana Tikku, Sean Brown, Jeremy Mendel	In this paper, we present a speech-driven second screen application for TV program discovery.
418	Clustering Species Accumulation Curves to Identify Skill Levels of Citizen Scientists Participating in the eBird Project	Jun Yu, Weng-Keen Wong, Steve Kelling	We propose a mixture model for clustering species accumulation curves.
419	StrokeBank: Automating Personalized Chinese Handwriting Generation	Alfred Zong, Yuke Zhu	In this paper, we propose StrokeBank, a novel approach to automating personalized Chinese handwriting generation.
420	DOROTHY: Enhancing Bidirectional Communication between a 3D Programming Interface and Mobile Robots	Emilie Featherston, Mohan Sridharan, Susan Urban, Joseph Urban	This paper summarizes the key capabilities of Dorothy, and describes the contributions made to: (a) enhance the bidirectional communication between the virtual interface and robots; and (b) support multirobot collaboration.
421	Shallow Blue: Lego-Based Embodied AI as a Platform for Cross-Curricular Project Based Learning	Robert Selkowitz, Debra T Burhans	We report on Shallow Blue (SB), an autonomous chess agent constructed by a small group of faculty and undergraduate students at Canisius College.
422	Teaching With Watson	Michael Wollowski	In this paper, we describe how we integrated the materials from the 2013 IBM The Great Minds Challenge (TGMC) – Watson Technical Edition into our Introductory Artificial Intelligence course.
423	Jim: A Platform for Affective AI in an Interdisciplinary Setting	Robert Selkowitz, Michael Heilemann, Jon Mrowczynski	We report on Jim, an inexpensive student designed platform for embodied affective AI.
424	Easychair as a Pedagogical Tool: Engaging Graduate Students in the Reviewing Process	Kartik Talamadupula, Subbarao Kambhampati	In this paper, we describe a class project that uses the popular Easychair conference management system as a pedagogical tool to enable engagement in the peer review process.
425	Model AI Assignments 2014	Todd W. Neller, Laura E. Brown, Roger L. West, James Heliotis, Sean Strout, Ivona Bezakova, Bikramjit Banerje, Daniel Thompson	Recognizing that assignments form the core of student learning experience, we here present abstracts of five AI assignments from the 2014 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.
426	Making CP-Nets (More) Useful	Thomas E. Allen	I discuss recent research in which we presented a novel algorithm for learning CP-nets from user queries, as well as work showing how to adapt existing algorithms to learn and reason with multivalued CP-nets that can model indifference as well as strict preference.
427	Information Sharing for Care Coordination	Ofra Amir	My research aims at developing agents that are able to make intelligent information sharing decisions to support a diverse, evolving team of care providers in constructing and maintaining a shared plan that operates in uncertain environments.
428	The Effect of Similarity between Human and Machine Action Choices on Adaptive Automation Performance	Jason M Bindewald	This research seeks to improve system control hand-offs, by investigating how the manner in which the automation completes its task affects the overall performance of the human-machine team.
429	Solving Semantic Problems Using Contexts Extracted from Knowledge Graphs	Adrian Boteanu	We introduce graph algorithms to assess the analogy strength in contexts derived from the analogy words.
430	Reinforcement Learning on Multiple Correlated Signals	Tim Brys, Ann Nowé	This extended abstract provides a brief overview of my PhD research on multi-objectivization and ensemble techniques in reinforcement learning.
431	Analogy Tutor: A Tutoring System for Promoting Conceptual Learning via Comparison	Maria de los Angeles Chang	A major challenge in artificial intelligence is building intelligent, interactive learning environments that can support students in human-like ways.
432	Imputation, Social Choice, and Partial Preferences	John A. Doucette	Imputation, Social Choice, and Partial Preferences
433	Robot Team Exploration with Communication Restrictions	Elizabeth A. Jensen	We have developed a set of distributed algorithms that make use of a small number of robots to fully explore an unknown environment even with restrictions on communication, team size, and available sensors.
434	The Semantic Interpretation of Trust in Multiagent Interactions	Anup Kumar Kalia	We provide an approach to estimate trust between agents from their interactions.
435	Modeling Argumentation and Explanation in the Social Web	Taraneh Khazaei	This manuscript provides the research questions, proposed research plans, as well as expected contributions of my doctoral dissertation.
436	Automatically Creating Multilingual Lexical Resources	Khang Nhut Lam	The thesis proposes creating bilingual dictionaries andWordnets for languages without many lexical resourcesusing resources of resource-rich languages.
437	Probabilistic Planning with Reduced Models	Luis Enrique Pineda	In my dissertation work I introduce a new paradigm to handle this complexity by deﬁning a family of MDP reduced models characterized by two parameters: the maximum number of primary outcomes per action that are fully accounted for and the maximum number of occurrences of the remaining exceptional outcomes that are planned for in advance.
438	Roles and Teams Hedonic Games	Matthew Jordan Spradling	We have introduced a new model of hedonic coalition formation game, which we call Roles and Teams Hedonic Games (RTHG).
439	Compilation Based Approaches to Probabilistic Planning — Thesis Summary	Ran Taig	In particular, we are exploring compilation techniques that allow us to reduce some probabilistic planning problems into variants of classical planning, such as metric planning,resource-bounded planning, and cost-bounded suboptimal planning.
440	Living and Searching in the World: Object-Based State Estimation for Mobile Robots	Lawson L. S. Wong	In my thesis, I propose a representation framework based on objects, their ‘semantic’ attributes, and their geometric realizations in the physical world.
441	Optimizing and Learning Diffusion Behaviors in Complex Network	Xiaojian Wu	This document describes my current research progress and future research directions of answering these two important questions.
442	Social Capital in Network Organizations	Saad Alqithami, Henry Hexmoor	This paper will outline the effect of social capital on a network structure inside a network organization.
443	To Share or Not to Share? The Single Agent in a Team Decision Problem	Ofra Amir, Barbara J. Grosz, Roni Stern	The paper proposes a novel integrated logical-decision-theoretic approach to solving SATD problems, called MDP-PRT.
444	Monte-Carlo Simulation Adjusting	Nobuo Araki, Masakazu Muramatsu, Hoki Kunihito, Satoshi Takahashi	In this paper, we propose a new learning method sim- ulation adjusting that adjusts simulation policy to im- prove the move decisions of the Monte Carlo method.
445	Advice Provision for Choice Selection Processes with Ranked Options	Amos Azaria, Ya'akov Gal, Claudia V. Goldman, Sarit Kraus	We propose several approaches to model human decision making in such settings.
446	A Knowledge Representation that Models Memory in Narrative Comprehension	Rogelio Enrique Cardona-Rivera, Robert Michael Young	We present work toward computationally defining a model of narrative comprehension vis-à-vis memory of narrative events, via an automated planning knowledge representation, capable of being used in a narrative generation context.
447	Association Rule Hiding Based on Evolutionary Multi-Objective Optimization by Removing Items	Peng Cheng, Jeng-Shyang Pan	In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization.
448	A Model for Aggregating Contributions of Synergistic Crowdsourcing Workflows	Yili Fang, Hailong Sun, Richong Zhang, Jinpeng Huai, Yongyi Mao	In this paper, we propose an assembly model to integrate the best output of subtasks from different workflows.
449	Online Search Algorithm Configuration	Tadhg Fitzgerald, Barry O'Sullivan, Yuri Malitsky, Kevin Tierney	This paper outlines an online approach for algorithm configuration which uses the power of modern multicore system to evaluate multiple parameters configurations in parallel.
450	Addressing Complexity in Multi-Issue Negotiation via Utility Hypergraphs	Rafik Hadfi, Takayuki Ito	We evaluated our model using parametrized random hyper- graphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches.
451	Communication-Restricted Exploration for Small Teams	Elizabeth A. Jensen, Ken Sugawara	We propose a novel algorithm that allows a small team of robots to fully explore an unknown environment, even in the face of extreme communication restrictions.
452	Genotypic versus Behavioural Diversity for Teams of Programs under the 4-v-3 Keepaway Soccer Task	Stephen Kelly, Malcolm I Heywood	In this work, a symbiotic framework for evolving teams of programs is utilized with both genotypic and behavioural forms of diversity maintenance considered.
453	A Novel Single-DBN Generative Model for Optimizing POMDP Controllers by Probabilistic Inference	Igor Kiselev, Pascal Poupart	As a promising alternative to using standard (often intractable) planning techniques with Bellman equations, we propose an interesting method of optimizing POMDP controllers by probabilistic inference in a novel equivalent single-DBN generative model.
454	Partial Satisfaction Planning under Time Uncertainty with Control on When Objectives Can Be Aborted	Sylvain Labranche, éric Beaudry	Our approach introduces special actions to explicitly abort objectives.
455	Semantical Clustering of Morphologically Related Chinese Words	Chia-Ling Lee, Ya-Ning Chang, Chao-Lin Liu, Chia-Ying Lee, Jane Yung-jen Hsu	In this paper, we aim at semantical clustering of a given family of morphologically related Chinese words.
456	Crowdsourced Explanations for Humorous Internet Memes	Chi-Chin Lin, Jane Yung-jen Hsu	In this work, we develop a system leveraging crowdsourcing technique to generate explanations for meme images.
457	LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs	Dawei Liu, Yuanzhuo Wang, Yantao Jia, Jingyuan Li, Zhihua Yu	We proposed a fast hashing approach called Locality-sensitive Social Distance Hashing (LSDH), which works in an unsupervised setup and performs approximate near neighbor search without high-dimensional distance computation.
458	Identifying Domain-Dependent Influential Microblog Users: A Post-Feature Based Approach	Nian Liu, Lin Li, Guandong Xu, Zhenglu Yang	In this paper, we focus on the problem of identifying domain-dependent influential users(or topic experts).
459	RepRev: Mitigating the Negative Effects of Misreported Ratings	Yuan Liu, Siyuan Liu, Jie Zhang, Hui Fang, Han Yu, Chunyan Miao	In this work, we propose a mechanism to mitigate the negativeeffect of the misreported ratings.
460	Reputation-Aware Continuous Double Auction	Yuan Liu, Jie Zhang, Han Yu, Chunyan Miao	In this paper, we propose a novel reputation-aware CDA (named RCDA) mechanism to consider the honesty of auction participants.
461	Computing Preferences Based on Agents’ Beliefs	Jian Luo, Fuan Pu, Yulai Zhang, Guiming Luo	The knowledgebase uncertainty and the argument preferences are considered in this paper.
462	Event Recommendation in Event-Based Social Networks	Zhi Qiao, Peng Zhang, Chuan Zhou, Yanan Cao, Li Guo, Yanchuan Zhang	In this paper, we present a baysian probability model that can fully unleash the power of heterogenous social relations and efficiently tackle with implicit feedback characteristic for event recommendation.
463	Coordination of Multiple Teams of Robots for an Optimal Global Plan	Zeynep Gozen Saribatur, Esra Erdem, Volkan Patoglu	We propose to solve this problem using answer set programming.
464	Inference Graphs: A New Kind of Hybrid Reasoning System	Daniel R. Schlegel, Stuart C. Shapiro	We outline a system which combines natural deduction and subsumption reasoning using Inference Graphs implementing a Logic of Arbitrary and Indefinite Objects.
465	Online Multi-Task Gradient Temporal-Difference Learning	Vishnu Purushothaman Sreenivasan, Haitham Bou Ammar, Eric Eaton	We develop an online multi-task formulation of model-based gradient temporal-difference (GTD) reinforcement learning.
466	A Data Complexity Approach to Kernel Selection for Support Vector Machines	Roberto Valerio, Ricardo Vilalta	We describe a data complexity approach to kernel selection based on the behavior of polynomial and Gaussian kernels.
467	A Model Attention and Selection Framework for Estimation of Many Variables, with Applications to Estimating Object States in Large Spatial Environments	Lawson L.S. Wong	In this work, I argue that state estimation should no longer be treated as a black box.
468	Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies	Jia Xu, Ubbo Visser, Mansur Kabuka	In this paper, we propose a revised most specific concept (MSC) method for DL SHI}, which converts instance checking into subsumption problems.
469	Uncovering Hidden Structure through Parallel Problem Decomposition	Yexiang Xue, Stefano Ermon, Carla Gomes, Bart Selman	In this paper, we introduce a novel way in which parallelism can be used to exploit hidden structure of hard combinatorial problems.
470	Representing Words as Lymphocytes	Jinfeng Yang, Yi Guan, Xishuang Dong, Bin He	Representing Words as Lymphocytes
471	Data Clustering by Laplacian Regularized L1-Graph	Yingzhen Yang, Zhangyang Wang, Jianchao Yang, Jiangping Wang, Shiyu Chang, Thomas S Huang	Motivated by L1-Graph and manifold leaning, we propose Laplacian Regularized L1-Graph (LRℓ1-Graph) for data clustering.
472	Fast Algorithm for Non-Stationary Gaussian Process Prediction	Yulai Zhang, Guiming Luo	Algorithm’s time complexity is an essential issue for time series prediction in numerous practices.A novel fast exact inference method for Gaussian process model is proposed in this paper to accelerate the task of non-stationary time series prediction.
473	Inferring Causal Directions in Errors-in-Variables Models	Yulai Zhang, Guiming Luo	A method for errors-in-variables models where both the cause variable and the effect variable are observed with measurement errors is presented in this paper.
474	Content-Structural Relation Inference in Knowledge Base	Zeya Zhao, Yantao Jia, Yuanzhuo Wang	In this paper, we propose a content-structural relation inference method (CSRI) which integrates the content and structural information between concepts for relation inference.