Paper Digest: AAAI 2015 Highlights

January 24, 2015October 8, 2019 admin

The AAAI Conference on Artificial Intelligence (AAAI) is one of the top artificial intelligence conferences in the world. In 2015, it is to be held in Austion, Texas.

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

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

Paper Digest Team
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TABLE 1: AAAI 2015 Papers

	Title	Authors	Highlight
1	Efficient Top-k Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling	Takuya Akiba, Takanori Hayashi, Nozomi Nori, Yoichi Iwata, Yuichi Yoshida	We propose an indexing scheme for top-k shortest-path distance queries on graphs, which is useful in a wide range of important applications such as network-aware search and link prediction.
2	Inferring Same-As Facts from Linked Data: An Iterative Import-by-Query Approach	Mustafa Al-Bakri, Manuel Atencia, Steffen Lalande, Marie-Christine Rousset	In this paper we model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data.
3	A Personalized Interest-Forgetting Markov Model for Recommendations	Jun Chen, Chaokun Wang, Jianmin Wang	In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model.
4	Will You “Reconsume” the Near Past? Fast Prediction on Short-Term Reconsumption Behaviors	Jun Chen, Chaokun Wang, Jianmin Wang	Will You “Reconsume” the Near Past? Fast Prediction on Short-Term Reconsumption Behaviors
5	VELDA: Relating an Image Tweet’s Text and Images	Tao Chen, Hany M. SalahEldeen, Xiangnan He, Min-Yen Kan, Dongyuan Lu	We develop Visual-Emotional LDA (VELDA), a novel topic model to capturethe image-text correlation from multiple perspectives (namely, visual and emotional).
6	On Information Coverage for Location Category Based Point-of-Interest Recommendation	Xuefeng Chen, Yifeng Zeng, Gao Cong, Shengchao Qin, Yanping Xiang, Yuanshun Dai	We develop a greedy algorithm and further optimization to solve this challenging problem.
7	Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data	Yan-Ying Chen, Yin-Hsi Kuo, Chun-Che Wu, Winston H. Hsu	We propose to associate a name and its likely demographic attributes by exploiting click-throughs between name queries and images with automatically detected facial attributes.
8	A New Granger Causal Model for Influence Evolution in Dynamic Social Networks: The Case of DBLP	Belkacem Chikhaoui, Mauricio Chiazzaro, Shengrui Wang	This paper addresses a new problem concerning the evolution of influence relationships between communities in dynamic social networks.
9	An Axiomatic Approach to Link Prediction	Sara Cohen, Aviv Zohar	We draw upon the motivation used in characterizations of ranking algorithms, as well as other celebrated results from social choice, and present an axiomatic basis for link prediction.
10	Perceiving Group Themes from Collective Social and Behavioral Information	Peng Cui, Tianyang Zhang, Fei Wang, Peng He	In this paper, we explicitly validate the interplay of collective social behavioral information and group themes using a large scale real dataset of online groups, and demonstrate the possibility of perceiving group themes from collective social and behavioral information.
11	Predicting the Demographics of Twitter Users from Website Traffic Data	Aron Culotta, Nirmal Ravi Kumar, Jennifer Cutler	In this paper, we predict the demographics of Twitter users based on whom they follow. Whereas most prior approaches rely on a supervised learning approach, in which individual users are labeled with demographics, we instead create a distantly labeled dataset by collecting audience measurement data for 1,500 websites (e.g., 50% of visitors to gizmodo.com are estimated to have a bachelor’s degree).
12	High-Performance Distributed ML at Scale through Parameter Server Consistency Models	Wei Dai, Abhimanu Kumar, Jinliang Wei, Qirong Ho, Garth Gibson, Eric P. Xing	Inspired by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models.
13	An EBMC-Based Approach to Selecting Types for Entity Filtering	Jiwei Ding, Wentao Ding, Wei Hu, Yuzhong Qu	In this paper, we propose a novel type selection approach based upon Budgeted Maximum Coverage (BMC), which can achieve integral optimization for the coverage quality of type filters.
14	Trust Models for RDF Data: Semantics and Complexity	Valeria Fionda, Gianluigi Greco	This paper embarks on a formal analysis of RDF data enriched with trust information by focusing on the characterization of its model-theoretic semantics and on the study of relevant reasoning problems.
15	Extended Property Paths: Writing More SPARQL Queries in a Succinct Way	Valeria Fionda, Giuseppe Pirrò, Mariano P. Consens	We introduce Extended Property Paths (EPPs), a significant enhancement of SPARQL property paths.
16	Lower and Upper Bounds for SPARQL Queries over OWL Ontologies	Birte Glimm, Yevgeny Kazakov, Ilianna Kollia, Giorgos Stamou	The paper presents an approach for optimizing the evaluation of SPARQL queries over OWL ontologies using SPARQL’s OWL Direct Semantics entailment regime.
17	FACES: Diversity-Aware Entity Summarization Using Incremental Hierarchical Conceptual Clustering	Kalpa Gunaratna, Krishnaparasad Thirunarayan, Amit Sheth	In this paper, we explore automatic summarization techniques that characterize and enable identification of an entity and create summaries that are human friendly.
18	TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings	Guibing Guo, Jie Zhang, Neil Yorke-Smith	To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique.
19	A Stochastic Model for Detecting Heterogeneous Link Communities in Complex Networks	Dongxiao He, Dayou Liu, Di Jin, Weixiong Zhang	We propose a stochastic model, which not only describes the structure of link communities, but also considers the heterogeneous distribution of community sizes, a property which is often ignored by other models.
20	Prajna: Towards Recognizing Whatever You Want from Images without Image Labeling	Xian-Sheng Hua, Jin Li	In this paper, we present an end-to-end Web knowledge discovery system, Prajna.
21	Kernel Density Estimation for Text-Based Geolocation	Mans Hulden, Miikka Silfverberg, Jerid Francom	In this paper we investigate an enhancement of common methods for determining the geographic point of origin of a text document by kernel density estimation.
22	Cross-Modal Image Clustering via Canonical Correlation Analysis	Cheng Jin, Wenhui Mao, Ruiqi Zhang, Yuejie Zhang, Xiangyang Xue	A new algorithm via Canonical Correlation Analysis (CCA) is developed in this paper to support more effective cross-modal image clustering for large-scale annotated image collections.
23	Modeling with Node Degree Preservation Can Accurately Find Communities	Di Jin, Zheng Chen, Dongxiao He, Weixiong Zhang	Here we address this critical problem.
24	Estimating Temporal Dynamics of Human Emotions	Seungyeon Kim, Joonseok Lee, Guy Lebanon, Haesun Park	In this paper, we extend sentiment and mood analysis temporally and model emotions as a function of time based on temporal streams of blog posts authored by a specific author.
25	Uniform Interpolation and Forgetting for ALC Ontologies with ABoxes	Patrick Koopmann, Renate A. Schmidt	We present the first method that can compute uniform interpolants of any ALC ontology with ABoxes.
26	Using Matched Samples to Estimate the Effects of Exercise on Mental Health via Twitter	Virgile Landeiro Dos Reis, Aron Culotta	In this work, we estimate the effect of exercise on mental health from Twitter, relying on statistical matching methods to reduce confounding bias.
27	Consistent Knowledge Discovery from Evolving Ontologies	Freddy Lecue, Jeff Z Pan	Consistent Knowledge Discovery from Evolving Ontologies
28	Multi-Document Summarization Based on Two-Level Sparse Representation Model	He Liu, Hongliang Yu, Zhi-Hong Deng	Based on the data reconstruction and sentence denoising assumption, we present a two-level sparse representation model to depict the process of multi-document summarization.
29	COT: Contextual Operating Tensor for Context-Aware Recommender Systems	Qiang Liu, Shu Wu, Liang Wang	In this work, we propose Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector.
30	Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network	Weiwei Liu, Zhi-Hong Deng, Xiuwen Gong, Frank Jiang, Ivor W. Tsang	Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network
31	Content-Based Collaborative Filtering for News Topic Recommendation	Zhongqi Lu, Zhicheng Dou, Jianxun Lian, Xing Xie, Qiang Yang	Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together.
32	A Tri-Role Topic Model for Domain-Specific Question Answering	Zongyang Ma, Aixin Sun, Quan Yuan, Gao Cong	In this paper, we propose a Tri-Role Topic Model (TRTM) to model the tri-roles of users (i.e., as askers, answerers, and voters, respectively) and the activities of each role including composing question, selecting question to answer, contributing and voting answers.
33	Handling Owl:sameAs via Rewriting	Boris Motik, Yavor Nenov, Robert Edgar Felix Piro, Ian Horrocks	We investigate issues related to both the correctness and efficiency of rewriting, and present an algorithm that guarantees correctness, improves efficiency, and can be effectively parallelised.
34	Incorporating Assortativity and Degree Dependence into Scalable Network Models	Stephen Mussmann, John Moore, Joseph John Pfeiffer, Jennifer Neville	The contributions of our work are twofold.
35	Using Description Logics for RDF Constraint Checking and Closed-World Recognition	Peter F. Patel-Schneider	Using Description Logics for RDF Constraint Checking and Closed-World Recognition
36	Approximating Model-Based ABox Revision in DL-Lite: Theory and Practice	Guilin Qi, Zhe Wang, Kewen Wang, Xuefeng Fu, Zhiqiang Zhuang	In this paper, we make both theoretical and practical contribution to efficient computation of model-based revisions in DL-Lite.
37	Leveraging Social Foci for Information Seeking in Social Media	Suhas Ranganath, Jiliang Tang, Xia Hu, Hari Sundaram, Huan Liu	In this paper, we propose a novel framework to identify the social connections of a user able to satisfy his information needs.
38	Extracting Bounded-Level Modules from Deductive RDF Triplestores	Marie-Christine Rousset, Federico Ulliana	We present a novel semantics for extracting bounded-level modules from RDF ontologies and databases augmented with safe inference rules, a la Datalog.
39	Question/Answer Matching for CQA System via Combining Lexical and Sequential Information	Yikang Shen, Wenge Rong, Zhiwei Sun, Yuanxin Ouyang, Zhang Xiong	In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers.
40	A Hybrid Approach of Classifier and Clustering for Solving the Missing Node Problem	Sigal Sina, Avi Rosenfeld, Sarit Kraus, Navot Akiva	In this paper, we propose a novel Hybrid Approach of Classifier and Clustering,a which we refer to as HACC, to solve the missing node identification problem in social networks.
41	Recommending Positive Links in Signed Social Networks by Optimizing a Generalized AUC	Dongjin Song, David A Meyer	To accomplish it, we propose ageneralized AUC (GAUC) to quantify the ranking performance ofpotential links (including positive, negative, and unknown statuslinks) in partially observed signed social networks.
42	Causal Inference via Sparse Additive Models with Application to Online Advertising	Wei Sun, Pengyuan Wang, Dawei Yin, Jian Yang, Yi Chang	In this paper we propose a novel two-stage causal inference framework for assessing the impact of complex ad treatments.
43	Sampling Representative Users from Large Social Networks	Jie Tang, Chenhui Zhang, Keke Cai, Li Zhang, Zhong Su	In this paper, we present a formal definition of the problem of \textbf{sampling representative users} from social network.
44	Relating Romanized Comments to News Articles by Inferring Multi-Glyphic Topical Correspondence	Goutham Tholpadi, Mrinal Kanti Das, Trapit Bansal, Chiranjib Bhattacharyya	In this paper, we extend the notion of correspondence to model multi-lingual, multi-script, and inter-lingual topics in a unified probabilistic model called the Multi-glyphic Correspondence Topic Model (MCTM).
45	Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets	Jinpeng Wang, Gao Cong, Xin Wayne Zhao, Xiaoming Li	In this paper, we propose to study the problem of identifying and classifying tweets into intent categories.
46	Burst Time Prediction in Cascades	Senzhang Wang, Zhao Yan, Xia Hu, Philip S. Yu, Zhoujun Li	To this end, this paper proposes a classification based approach for burst time prediction by utilizing and modeling rich knowledge in information diffusion.
47	Clustering-Based Collaborative Filtering for Link Prediction	Xiangyu Wang, Dayu He, Danyang Chen, Jinhui Xu	In this paper, we propose a novel collaborative filtering approach for predicting the unobserved links in a network (or graph) with both topological and node features.
48	Mining Query Subtopics from Questions in Community Question Answering	Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou	This paper proposes mining query subtopics from questions in community question answering (CQA).
49	DynaDiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization	Miao Xie, Qiusong Yang, Qing Wang, Gao Cong, Gerard de Melo	We introduce the continuous time constrained influence maximization problem for dynamic diffusion networks, based on a novel diffusion model called DynaDiffuse.
50	A Probabilistic Model for Bursty Topic Discovery in Microblogs	Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng	This work develops a novel probabilistic model, namely Bursty Biterm Topic Model (BBTM), to deal with the task.
51	On the Scalable Learning of Stochastic Blockmodel	Bo Yang, Xuehua Zhao	To address this issue, we present a novel SBM learning algorithm referred to as BLOS (BLOckwise Sbm learning).
52	RAIN: Social Role-Aware Information Diffusion	Yang Yang, Jie Tang, Cane Wing-ki Leung, Yizhou Sun, Qicong Chen, Juanzi Li, Qiang Yang	In this paper, we study the interplay between users’ social roles and their influence on information diffusion.
53	Collaborative Topic Ranking: Leveraging Item Meta-Data for Sparsity Reduction	Weilong Yao, Jing He, Hua Wang, Yanchun Zhang, Jie Cao	To alleviate this problem, in this paper, we propose a novel hierarchical Bayesian framework which incorporates “bag-of-words” type meta-data on items into pair-wise ranking models for one-class collaborative filtering.
54	Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks	Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang	Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN).
55	Are Features Equally Representative? A Feature-Centric Recommendation	Chenyi Zhang, Ke Wang, Ee-peng Lim, Qinneng Xu, Jianling Sun, Hongkun Yu	We demonstrate this approach by turning the traditional item-centric latent factor model into a feature-centric solution and demonstrate its superiority over item-centric approaches.
56	Incorporating Implicit Link Preference Into Overlapping Community Detection	Hongyi Zhang, Irwin King, Michael R. Lyu	In this paper, we propose a preference-based nonnegative matrix factorization (PNMF) model to incorporate implicit link preference information.
57	Retweet Behavior Prediction Using Hierarchical Dirichlet Process	Qi Zhang, Yeyun Gong, Ya Guo, Xuanjing Huang	In this work, we propose a novel method using non-parametric statistical models to combine structural, textual, and temporal information together to predict retweet behavior.
58	Exploring Key Concept Paraphrasing Based on Pivot Language Translation for Question Retrieval	Wei-Nan Zhang, Zhao-Yan Ming, Yu Zhang, Ting Liu, Tat-Seng Chua	In this paper, we explore a pivot language translation based approach to derive the paraphrases of key concepts.
59	Representation Learning for Aspect Category Detection in Online Reviews	Xinjie Zhou, Xiaojun Wan, Jianguo Xiao	In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection.
60	Person Identification Using Anthropometric and Gait Data from Kinect Sensor	Virginia Ortiz Andersson, Ricardo Matsumura Araujo	In this paper, we report on experiments using a novel data set composed of 140 individuals walking in front of a Microsoft Kinect sensor.
61	R1SVM: A Randomised Nonlinear Approach to Large-Scale Anomaly Detection	Sarah M. Erfani, Mahsa Baktashmotlagh, Sutharshan Rajasegarar, Shanika Karunasekera, Chris Leckie	In this paper we leverage the theoryof nonlinear random projections and propose the RandomisedOne-class SVM (R1SVM), which is an efficient and scalableanomaly detection technique that can be trained on large-scale datasets.
62	Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel	Xiaomin Fang, Rong Pan, Guoxiang Cao, Xiuqiang He, Wenyuan Dai	In this paper, we propose a novel method for personalized tag recommendation, which can be considered as a nonlinear extension of Canonical Decomposition.
63	A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data	Marzyeh Ghassemi, Marco A.F. Pimentel, Tristan Naumann, Thomas Brennan, David A. Clifton, Peter Szolovits, Mengling Feng	We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes.
64	Probabilistic Graphical Models for Boosting Cardinal and Ordinal Peer Grading in MOOCs	Fei Mi, Dit-Yan Yeung	In this paper, we seek to study both cardinal and ordinal aspects of peer grading within a common framework.
65	Efficient Computation of Semivalues for Game-Theoretic Network Centrality	Piotr Lech Szczepański, Mateusz Krzysztof Tarkowski, Tomasz Paweł Michalak, Paul Harrenstein, Michael Wooldridge	In an attempt to address the computational issues of game-theoretic network centrality, we present a generic framework for constructing game-theoretic network centralities.
66	Embedded Unsupervised Feature Selection	Suhang Wang, Jiliang Tang, Huan Liu	In this paper, we propose a novel unsupervisedfeature selection algorithm EUFS, which directlyembeds feature selection into a clustering algorithm viasparse learning without the transformation.
67	Learning User-Specific Latent Influence and Susceptibility from Information Cascades	Yongqing Wang, Huawei Shen, Shenghua Liu, Xueqi Cheng	Here we propose to model the cascade dynamics by learning two low-dimensional user-specific vectors from observed cascades, capturing their influence and susceptibility respectively.
68	Kickback Cuts Backprop’s Red-Tape: Biologically Plausible Credit Assignment in Neural Networks	David Balduzzi, Hastagiri Vanchinathan, Joachim Buhmann	In this paper, we revisit Backprop and the credit assignment problem.
69	An Agent-Based Model of the Emergence and Transmission of a Language System for the Expression of Logical Combinations	Josefina Sierra-Santibanez	This paper presents an agent-based model of the emergence and transmission of a language system for the expression of logical combinations of propositions.
70	Moral Decision-Making by Analogy: Generalizations versus Exemplars	Joseph A. Blass, Kenneth D. Forbus	This paper explores the use of analogical generalizations to improve moral reasoning.
71	AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis	Erik Cambria, Jie Fu, Federica Bisio, Soujanya Poria	AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis
72	Dialogue Understanding in a Logic of Action and Belief	Alfredo Gabaldon, Pat Langley	In this paper, we analyze both aspects of the architecture in terms of the Situation Calculus — a classicallogic for reasoning about dynamical systems — and give a specification of the inference task the system performs.
73	Automated Construction of Visual-Linguistic Knowledge via Concept Learning from Cartoon Videos	Jung-Woo Ha, Kyung-Min Kim, Byoung-Tak Zhang	We present the model of deep concept hierarchy (DCH) that enables the progressive abstraction of concept knowledge in multiple levels.
74	Bayesian Affect Control Theory of Self	Jesse Hoey, Tobias Schroeder	We propose here a Bayesian generalization of the sociological affect control theory of self as a theoretical foundation for socio-affectively skilled artificial agents.
75	Heuristic Induction of Rate-Based Process Models	Pat Langley, Adam Arvay	This paper presents a novel approach to inductive process modeling, the task of constructing a quantitative account of dynamical behavior from time-series data and background knowledge.
76	Spontaneous Retrieval from Long-Term Memory for a Cognitive Architecture	Justin Li, John Laird	This paper presents the first functional evaluation of spontaneous, uncued retrieval from long-term memory in a cognitive architecture.
77	Learning Plausible Inferences from Semantic Web Knowledge by Combining Analogical Generalization with Structured Logistic Regression	Chen Liang, Kenneth D. Forbus	This paper applies structured logistic regression with analogical generalization (SLogAn) to make use of structural as well as statistical information to achieve rapid and robust learning.
78	Ontology-Based Information Extraction with a Cognitive Agent	Peter Lindes, Deryle W. Lonsdale, David W. Embley	In this paper we describe a machine reading system that we have developed within a cognitive architecture.
79	Extending Analogical Generalization with Near-Misses	Matthew D. McLure, Scott E. Friedman, Kenneth D. Forbus	This paper introduces Analogical Learning by Integrating Generalization and Near-misses (ALIGN) and describes three key advances.
80	Automatic Ellipsis Resolution: Recovering Covert Information from Text	Marjorie McShane, Petr Babkin	The key insight of the work presented here is that not all cases of ellipsis are equally difficult: some can be detected and resolved with high confidence even before we are able to build agents with full human-level semantic and pragmatic understanding of text.
81	Inference Graphs: Combining Natural Deduction and Subsumption Inference in a Concurrent Reasoner	Daniel R. Schlegel, Stuart C Shapiro	Inference Graphs: Combining Natural Deduction and Subsumption Inference in a Concurrent Reasoner
82	An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building	Miaolong Yuan, Bo Tian, Vui Ann Shim, Huajin Tang, Haizhou Li	This paper presents a novel computational model to build cognitive maps of real environments using both place cells and grid cells.
83	An Association Network for Computing Semantic Relatedness	Keyang Zhang, Kenny Zhu, Seung-won Hwang	We propose to expand lexical coverage and overcome sparseness by constructing an association network of terms and concepts that combines signals from free association norms and five types of co-occurrences extracted from therich structures of Wikipedia.
84	Influence-Driven Model for Time Series Prediction from Partial Observations	Saima Aman, Charalampos Chelmis, Viktor K. Prasanna	We propose a novel solution to the problem of making short term predictions in absence of real-time data from sensors.
85	Sharing Rides with Friends: A Coalition Formation Algorithm for Ridesharing	Filippo Bistaffa, Alessandro Farinelli, Sarvapali D. Ramchurn	We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, arrange one-time rides at short notice.
86	Best-Response Planning of Thermostatically Controlled Loads under Power Constraints	Frits de Nijs, Matthijs T. J. Spaan, Mathijs M. de Weerdt	In this paper we investigate the use of planning under uncertainty to pro-actively control an aggregation of TCLs to overcome temporary grid imbalance.
87	FutureMatch: Combining Human Value Judgments and Machine Learning to Match in Dynamic Environments	John P. Dickerson, Tuomas Sandholm	Motivated by our experience running the computational side of a large nationwide kidney exchange, we present FutureMatch, a framework for learning to match in a general dynamic model.
88	Energy Disaggregation via Learning Powerlets and Sparse Coding	Ehsan Elhamifar, Shankar Sastry	In this paper, we consider the problem of energy disaggregation, i.e., decomposing a whole home electricity signal into its component appliances.
89	Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery	Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla P. Gomes, Bart Selman, Robert B. van Dover	Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints.
90	Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa	Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick Clark, Steve DeGloria, Andrew Mude, Christopher Barrett, Carla P. Gomes	In this paper we consider the problem of inferring agents’ preferences from observed movement trajectories, and formulate it as an Inverse Reinforcement Learning (IRL) problem .
91	A Nonparametric Online Model for Air Quality Prediction	Vitor Campanholo Guizilini, Fabio Tozeto Ramos	We introduce a novel method for the continuous online prediction of particulate matter in the air (more specifically, PM10 and PM2.5) given sparse sensor information.
92	Power System Restoration With Transient Stability	Hassan Hijazi, Terrence W.K. Mak, Pascal Van Hentenryck	In this paper, we show how to integrate transient stability in the optimization procedure by capturing the rotor dynamics of power generators.
93	Aggregating Electric Cars to Sustainable Virtual Power Plants: The Value of Flexibility in Future Electricity Markets	Micha Kahlen, Wolfgang Ketter	We developed an algorithm that determines for a fleet of electric vehicles, which EV at what price and location to commit to the operating reserve market to either absorb excess capacity or provide electricity during shortages (vehicle-2-grid).
94	Energy Usage Behavior Modeling in Energy Disaggregation via Marked Hawkes Process	Liangda Li, Hongyuan Zha	To model such relationship, we combine topic models with Hawkes processes, and propose a novel probabilistic model based on marked Hawkes process that enables the modeling of marked event data.
95	HVAC-Aware Occupancy Scheduling	BoonPing Lim, Menkes van den Briel, Sylvie Thiebaux, Scott Backhaus, Russell Bent	We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approaches.
96	Data Analysis and Optimization for (Citi)Bike Sharing	Eoin O'Mahony, David B. Shmoys	In this paper, we tackle the problem of maintaing system balance during peak rush-hour usageas well as rebalancing overnight to prepare the systemfor rush-hour usage.
97	Towards Optimal Solar Tracking: A Dynamic Programming Approach	Athanasios Aris Panagopoulos, Georgios Chalkiadakis, Nicholas Robert Jennings	In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from on-line providers.
98	Risk Based Optimization for Improving Emergency Medical Systems	Sandhya Saisubramanian, Pradeep Varakantham, Hoong Chuin Lau	To allow for ”live” reallocation of ambulances, we provide a decomposition method based on Lagrangian Relaxation to significantly reduce the run-time of the optimization formulation.Finally, we provide an exhaustive evaluation on real-world datasets from two asian cities that demonstrates the improvement provided by our approach over current practice and the best known approach from literature.
99	Predisaster Preparation of Transportation Networks	Hermann Schichl, Meinolf Sellmann	In particular, we introduce a new type of extreme resource constraint and develop a practically efficient propagation algorithm for it.
100	SmartShift: Expanded Load Shifting Incentive Mechanism for Risk-Averse Consumers	Bochao Shen, Balakrishnan Narayanaswamy, Ravi Sundaram	We present a formal framework for the incentive model that is applicable to different forms of the electricity market.
101	Incentivizing Users for Balancing Bike Sharing Systems	Adish Singla, Marco Santoni, Gábor Bartók, Pratik Mukerji, Moritz Meenen, Andreas Krause	In this paper, we address this question and present a crowdsourcing mechanism that incentivizes the users in the bike repositioning process by providing them with alternate choices to pick or return bikes in exchange for monetary incentives.
102	A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data	Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, Xing Xie	Hence, in this paper,we collect big and heterogeneous data (e.g. 1.6 million users’ GPS records in three years, 17520 times of Japan earthquake data in four years, news reporting data, transportation network data and etc.) to capture and analyze human emergency mobility following different disasters.
103	Real-Time Predictive Optimization for Energy Management in a Hybrid Electric Vehicle	Alexander David Styler, Illah Reza Nourbakhsh	In this work, we propose and evaluate a novel, real-time optimization strategy that leverages predictions from prior data in a simulated hybrid battery-supercapacitor energy management task.
104	Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games	Umair Z. Ahmed, Krishnendu Chatterjee, Sumit Gulwani	In this paper, we address the problem of generating targeted starting positions for such games.
105	Continuity Editing for 3D Animation	Quentin Galvane, Rémi Ronfard, Christophe Lino, Marc Christie	We describe an optimization-based approach for automatically creating well-edited movies from a 3D animation.
106	Assessing the Robustness of Cremer-McLean with Automated Mechanism Design	Michael Albert, Vincent Conitzer, Giuseppe Lopomo	In this paper, we use an automated mechanism design approach to assess how sensitive the Cremer-McLean result is to relaxing its main technical assumption.
107	Online Learning and Profit Maximization from Revealed Preferences	Kareem Amin, Rachel Cummings, Lili Dworkin, Michael Kearns, Aaron Roth	We consider the problem of learning from revealed preferences in an online setting.
108	Approximating Optimal Social Choice under Metric Preferences	Elliot Anshelevich, Onkar Bhardwaj, John Postl	We examine the quality of social choice mechanisms using a utilitarian view, in which all of the agents have costs for each of the possible alternatives.
109	Justified Representation in Approval-Based Committee Voting	Haris Aziz, Markus Brill, Vincent Conitzer, Edith Elkind, Rupert Freeman, Toby Walsh	We propose a natural axiom for this setting, which we call justified representation (JR).
110	Audit Games with Multiple Defender Resources	Jeremiah Blocki, Nicolas Christin, Anupam Datta, Ariel D. Procaccia, Arunesh Sinha	We significantly generalize this audit game model to account for multiple audit resources where each resource is restricted to audit a subset of all potential violations, thus enabling application to practical auditing scenarios.
111	Learning Valuation Distributions from Partial Observation	Avrim Blum, Yishay Mansour, Jame Morgenstern	In this work, we consider the problem of learning bidders’ val- uation distributions from much weaker forms of obser- vations.
112	Sequence-Form Algorithm for Computing Stackelberg Equilibria in Extensive-Form Games	Branislav Bosansky, Jiri Cermak	We extend the existing algorithmic work to extensive-form games and introduce novel algorithm for computing Stackelberg equilibria that exploits the compact sequence-form representation of strategies.
113	Combining Compact Representation and Incremental Generation in Large Games with Sequential Strategies	Branislav Bosansky, Albert Xin Jiang, Milind Tambe, Christopher Kiekintveld	In this paper, we present novel hybrid of these two approaches: compact-strategy double-oracle (CS-DO) algorithm that combines the advantages of the compact representation with incremental strategy generation.
114	Strategic Voting and Strategic Candidacy	Markus Brill, Vincent Conitzer	In this paper, we extend the analysis to also include strategic behavior on the part of the voters.
115	A Faster Core Constraint Generation Algorithm for Combinatorial Auctions	Benedikt Bünz, Sven Seuken, Benjamin Lubin	In this paper, we present a new algorithm that significantly outperforms the current state of the art.
116	Price Evolution in a Continuous Double Auction Prediction Market With a Scoring-Rule Based Market Maker	Mithun Chakraborty, Sanmay Das, Justin Peabody	In this paper, we study the properties of CDA prediction markets with zero-intelligence traders in which an LMSR-style market maker participates actively.
117	Computing Nash Equilibrium in Interdependent Defense Games	Hau Chan, Luis Ortiz	In this paper, our focus is the study of the problem of computing a Nash Equilibrium (NE) in IDD games.
118	Fair Information Sharing for Treasure Hunting	Yiling Chen, Kobbi Nissim, Bo Waggoner	In order to formalize the planner’s goals of fairness and reduced search cost, we propose a simplified, simulated game as a benchmark and quantify fairness and search cost relative to this benchmark scenario.
119	Conventional Machine Learning for Social Choice	John A. Doucette, Kate Larson, Robin Cohen	We show that suitable predictive features can be extracted from the data, and demonstrate the high performance of our new framework on the ballots from many real world elections, including comparisons with existing techniques for voting with partial orderings.
120	The Complexity of Recognizing Incomplete Single-Crossing Preferences	Edith Elkind, Piotr Faliszewski, Martin Lackner, Svetlana Obraztsova	We study the complexity of deciding if a given profile of incomplete votes (i.e., a profile of partial orders over a given set of alternatives) can be extended to a single-crossing profile of complete votes (total orders).
121	A Unifying Hierarchy of Valuations with Complements and Substitutes	Uriel Feige, Michal Feldman, Nicole Immorlica, Rani Izsak, Brendan Lucier, Vasilis Syrgkanis	We introduce a new hierarchy over monotone set functions, that we refer to as MPH (Maximum over Positive Hypergraphs).
122	Do Capacity Constraints Constrain Coalitions?	Michal Feldman, Ofir Geri	In this work we combine these variants and analyze strong equilibria (profiles where no coalition can deviate) in capacitated games.
123	A Mechanism Design Approach to Measure Awareness	Diodato Ferraioli, Carmine Ventre, Gabor Aranyi	In this paper, we study protocols that allow to discern conscious and unconscious decisions of human beings; i.e., protocols that measure awareness.
124	Facility Location with Double-Peaked Preferences	Aris Filos-Ratsikas, Minming Li, Jie Zhang, Qiang Zhang	As our main contribution, we present a simple truthful-in-expectation mechanism that achieves an approximation ratio of 1+b/c for both the social and the maximum cost, where b is the distance of the agent from the peak and c is the minimum cost of an agent.
125	Elicitation for Aggregation	Rafael M. Frongillo, Yiling Chen, Ian A. Kash	We study the problem of eliciting and aggregating probabilistic information from multiple agents.
126	A Complexity Approach for Core-Selecting Exchange with Multiple Indivisible Goods under Lexicographic Preferences	Etsushi Fujita, Julien Lesca, Akihisa Sonoda, Taiki Todo, Makoto Yokoo	We propose an exchange rule called augmented top-trading-cycles (ATTC) procedure based on the original TTC procedure.
127	Security Games with Protection Externalities	Jiarui Gan, Bo An, Yevgeniy Vorobeychik	On the positive side, we propose a novel column generation based approach—CLASPE—to solve SPEs.
128	Strategy-Proof and Efficient Kidney Exchange Using a Credit Mechanism	Chen Hajaj, John P. Dickerson, Avinatan Hassidim, Tuomas Sandholm, David Sarne	We present a credit-based matching mechanism for dynamic barter markets — and kidney exchange in particular — that is both strategy proof and efficient, that is, it guarantees truthful disclosure of donor-patient pairs from the transplant centers and results in the maximum global matching.
129	Hedonic Coalition Formation in Networks	Martin Hoefer, Daniel Vaz, Lisa Wagner	We capture and study this aspect using a novel network-based model for dynamic locality within the popular framework of hedonic coalition formation games.
130	Matching with Dynamic Ordinal Preferences	Hadi Hosseini, Kate Larson, Robin Cohen	We consider the problem of repeatedly matching a set of alternatives to a set of agents with dynamic ordinal preferences.
131	On a Competitive Secretary Problem	Anna Karlin, Eric Lei	In this paper, we address this question by studying a generalization of the classical secretary problem from optimal stopping theory: a set of ranked employers compete to hire from the same random stream of employees, and each employer wishes to hire the best candidate in the bunch.
132	Controlled School Choice with Soft Bounds and Overlapping Types	Ryoji Kurata, Masahiro Goto, Atsushi Iwasaki, Makoto Yokoo	Thus, we propose an alternative model and an alternative stability definition, where a school has reserved seats for each type.
133	Optimal Personalized Filtering Against Spear-Phishing Attacks	Aron Laszka, Yevgeniy Vorobeychik, Xenofon Koutsoukos	In this paper, we assume that a learned classifier is given and propose strategic per-user filtering thresholds for mitigating spear-phishing attacks.
134	Stable Invitations	Hooyeon Lee, Yoav Shoham	We consider the situation in which an organizer is trying to convenean event, and needs to choose whom out of a given set of agents to invite.Agents have preferences over how many attendees should be at the eventand possibly also who the attendees should be.This induces a stability requirement: All invited agents should preferattending to not attending, and all the other agents should not regretbeing not invited.The organizer’s objective is to find an invitation of maximum size,subject to the stability requirement.
135	The Pricing War Continues: On Competitive Multi-Item Pricing	Omer Lev, Joel Oren, Craig Boutilier, Jeffrey S. Rosenschein	We study a game with \emph{strategic} vendors (the agents) who own multiple items and a single buyer with a submodular valuation function.
136	Cooperative Game Solution Concepts that Maximize Stability under Noise	Yuqian Li, Vincent Conitzer	In this context, we investigate which solution concepts maximize the probability of ex-post stability (after the true values are revealed).
137	Congestion Games with Distance-Based Strict Uncertainty	Reshef Meir, David Parkes	We take a non-probabilistic approach, assuming that each agent knows that the number of agents using an edge is within a certain range.
138	On the Convergence of Iterative Voting: How Restrictive Should Restricted Dynamics Be?	Svetlana Obraztsova, Evangelos Markakis, Maria Polukarov, Zinovi Rabinovich, Nicholas R. Jennings	We study convergence properties of iterative voting procedures.
139	Voting Rules As Error-Correcting Codes	Ariel D. Procaccia, Nisarg Shah, Yair Zick	We present the first model of optimal voting under adversarial noise.
140	Analysis of Equilibria in Iterative Voting Schemes	Zinovi Rabinovich, Svetlana Obraztsova, Omer Lev, Evangelos Markakis, Jeffrey S. Rosenschein	We start with the basic model of plurality voting.
141	Incentives for Subjective Evaluations with Private Beliefs	Goran Radanovic, Boi Faltings	Nevertheless, we provide a modified version of a divergence-based Bayesian Truth Serum that incentivizes agents to report consistently, making truthful reporting a weak equilibrium of the mechanism.
142	Envy-Free Cake-Cutting in Two Dimensions	Erel Segal-Halevi, Avinatan Hassidim, Yonatan Aumann	We thus introduce and study the problem of envy-free two-dimensional division wherein the utility of the agents depends on the geometric shape of the allocated pieces (as well as the location and size).
143	Truthful Mechanisms without Money for Non-Utilitarian Heterogeneous Facility Location	Paolo Serafino, Carmine Ventre	In this paper, we consider the facility location problem un- der a novel model recently proposed in the literature, which combines the no-money constraint (i.e. the impossibility to employ monetary transfers between the mechanism and the agents) with the presence of heterogeneous facilities, i.e. facilities serving different purposes.
144	A Graphical Representation for Games in Partition Function Form	Oskar Skibski, Tomasz P. Michalak, Yuko Sakurai, Michael Wooldridge, Makoto Yokoo	We propose a novel representation for coalitional games with externalities, called Partition Decision Trees.
145	A Stackelberg Game Approach for Incentivizing Participation in Online Educational Forums with Heterogeneous Student Population	Rohith Dwarakanath Vallam, Priyanka Bhatt, Debmalya Mandal, Narahari Y.	In an effort to address this problem, we propose an incentive-based, instructor-driven approach to orchestrate the interactions in online educational forums (OEFs).
146	Mechanism Design for Team Formation	Mason Wright, Yevgeniy Vorobeychik	We present the first formal mechanism design framework for team formation, building on recent combinatorial matching market design literature.
147	Exploring Information Asymmetry in Two-Stage Security Games	Haifeng Xu, Zinovi Rabinovich, Shaddin Dughmi, Milind Tambe	In this paper, we propose an approach for improving the defender’s utility in such scenarios.
148	Balanced Trade Reduction for Dual-Role Exchange Markets	Dengji Zhao, Sarvapali D. Ramchurn, Enrico H. Gerding, Nicholas R. Jennings	Hence, to combat this problem, following McAfee’s trade reduction approach, we propose a new trade reduction mechanism, called balanced trade reduction, that is incentive compatible and also provides flexible trade-offs between efficiency and deficit.
149	Optimal Machine Strategies to Commit to in Two-Person Repeated Games	Song Zuo, Pingzhong Tang	In this paper, we consider the problem of computing optimal leader’s machine to commit to in two-person repeated game, where the follower also plays a machine strategy.
150	Optimal Column Subset Selection by A-Star Search	Hiromasa Arai, Crystal Maung, Haim Schweitzer	We show how to model the problem as a graph search, and propose a heuristic based on eigenvalues of related matrices.
151	Limitations of Front-To-End Bidirectional Heuristic Search	Joseph K. Barker, Richard E. Korf	We present an intuitive explanation for the limited effectiveness of front-to-end bidirectional heuristic search, supported with extensive evidence from many commonly-studied domains.
152	Incremental Weight Elicitation for Multiobjective State Space Search	Nawal Benabbou, Patrice Perny	This paper proposes incremental preference elicitation methods for multiobjective state space search.
153	Complexity Results for Compressing Optimal Paths	Adi Botea, Ben Strasser, Daniel Harabor	In this work we give a first tractability analysis of Compressed Path Databases, space efficient oracles used to very quickly identify the first arc on a shortest path.
154	Two Weighting Local Search for Minimum Vertex Cover	Shaowei Cai, Jinkun Lin, Kaile Su	In this paper, we propose a vertex weighting scheme to address this shortcoming, and combine it within the current best MinVC local search algorithm NuMVC, leading to a new algorithm called TwMVC.
155	Efficient Benchmarking of Hyperparameter Optimizers via Surrogates	Katharina Eggensperger, Frank Hutter, Holger Hoos, Kevin Leyton-Brown	In this work, we introduce another option: cheap-to-evaluate surrogates of real hyperparameter optimization benchmarks that share the same hyperparameter spaces and feature similar response surfaces.
156	Convergent Plans for Large-Scale Evacuations	Caroline Even, Victor Pillac, Pascal Van Hentenryck	This paper introduces the concept of convergent evacuation plans to tackle this issue.
157	Initializing Bayesian Hyperparameter Optimization via Meta-Learning	Matthias Feurer, Jost Tobias Springenberg, Frank Hutter	In this paper we mimic a strategy human domain experts use: speed up optimization by starting from promising configurations that performed well on similar datasets.
158	Stochastic Local Search for Satisfiability Modulo Theories	Andreas Fröhlich, Armin Biere, Christoph M. Wintersteiger, Youssef Hamadi	In this paper, we present a novel stochastic local search (SLS) algorithm to solve SMT problems, especially those in the theory of bit-vectors, directly on the theory level.
159	Lagrangian Decomposition Algorithm for Allocating Marketing Channels	Daisuke Hatano, Takuro Fukunaga, Takanori Maehara, Ken-ichi Kawarabayashi	In this paper, we formulate a new problem related to the well-known influence maximization in the context of computational advertising.
160	Recursive Best-First Search with Bounded Overhead	Matthew Hatem, Scott Kiesel, Wheeler Ruml	In this paper, we present two simple techniques for improving the performance of RBFS while maintaining its advantages over IDA*.
161	Reusing Previously Found A* Paths for Fast Goal-Directed Navigation in Dynamic Terrain	Carlos Hernandez, Roberto Asin, Jorge A Baier	In this paper we show how GAA* can be modified to exploit more information from a previous search in addition to the updated heuristic function.
162	Pruning Game Tree by Rollouts	Bojun Huang	In this paper we show that the alpha-beta algorithm and its successor MT-SSS*, as two classic minimax search algorithms, can be implemented asrollout algorithms, a generic algorithmic paradigm widely used in many domains.
163	Solving Distributed Constraint Optimization Problems Using Logic Programming	Tiep Le, Tran Cao Son, Enrico Pontelli, William Yeoh	This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs).
164	Value-Directed Compression of Large-Scale Assignment Problems	Tyler Lu, Craig Boutilier	Modeling such (generalized) assignment problems as linear programs, we propose a generalvalue-directed compression technique for solving such problems at scale.
165	On Unconstrained Quasi-Submodular Function Optimization	Jincheng Mei, Kang Zhao, Bao-Liang Lu	In this paper, we focus on quasi-submodularity, a universal generalization, which satisfies weaker properties than submodularity but still enjoys favorable performance in optimization.
166	Improved Local Search for Binary Matrix Factorization	Seyed Hamid Mirisaee, Eric Gaussier, Alexandre Termier	We show in particular that the proposed solution is in general faster than the previously proposed ones.
167	A Theoretical Analysis of Optimization by Gaussian Continuation	Hossein Mobahi, John W. Fisher III	Here, we provide a theoretical analysis that provides a bound on the endpoint solution of the continuation method.
168	Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization	Danny Munera, Daniel Diaz, Salvador Abreu, Francesca Rossi, Vijay Saraswat, Philippe Codognet	We address this problem using a local search technique, based on Adaptive Search and present experimental evidence that this approach is much more efficient than state-of-the-art exact and approximate methods.
169	BDD-Constrained Search: A Unified Approach to Constrained Shortest Path Problems	Masaaki Nishino, Norihito Yasuda, Shin-ichi Minato, Masaaki Nagata	The important feature of BCS is that it can be applied to problems with various types of logical constraints in a unified way once we represent the constraints as a BDD.
170	Exploiting Variable Associations to Configure Efficient Local Search in Large-Scale Set Partitioning Problems	Shunji Umetani	We present a data mining approach for reducing the search space of local search algorithms in large-scale set partitioning problems (SPPs).
171	Resilient Upgrade of Electrical Distribution Grids	Emre Yamangil, Russell Bent, Scott Backhaus	We formulate an optimal electrical distribution grid design problem as a two-stage, stochastic mixed-integer program with damage scenarios from natural disasters modeled as a set of stochastic events.
172	TDS+: Improving Temperature Discovery Search	Yeqin Zhang, Martin Müller	Temperature and mean are important concepts in combinatorial game theory, which can be used to develop efficient algorithms for playing well in a sum of subgames.
173	Massively Parallel A* Search on a GPU	Yichao Zhou, Jianyang Zeng	In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fashion.
174	Novel Mechanisms for Online Crowdsourcing with Unreliable, Strategic Agents	Praphul Chandra, Yadati Narahari, Debmalya Mandal, Prasenjit Dey	For this setting, we propose two mechanisms: a DPM (DynamicPrice Mechanism) and an ABM (Auction Based Mechanism).
175	Acquiring Speech Transcriptions Using Mismatched Crowdsourcing	Preethi Jyothi, Mark Hasegawa-Johnson	We follow an information-theoretic approach to this problem: (1) We learn the characteristics of a noisy channel that models the transcribers’ systematic perception biases.
176	Incentive Networks	Yuezhou Lv, Thomas Moscibroda	In this work, we study an alternative approach — Incentive Networks — in which a participant’s reward depends not only on his own contribution; but also in part on the contributions made by his social contacts or friends.
177	Collaboration in Social Problem-Solving: When Diversity Trumps Network Efficiency	Diego Noble, Marcelo Prates, Daniel Bossle, Luís Lamb	In this paper we analyse a recent social problem-solving model and attempt to address its shortcomings.
178	CrowdWON: A Modelling Language for Crowd Processes based on Workflow Nets	David Sanchez-Charles, Victor Muntes-Mulero, Marc Sole, Jordi Nin	In this paper, we propose CrowdWON, a new graphical framework to describe and monitor crowd processes, the proposed language is able to represent the workflow of most well-known existing applications, extend previous modelling frameworks, and assist in the future generation of crowdsourcing platforms.
179	On the Impossibility of Convex Inference in Human Computation	Nihar B. Shah, Dengyong Zhou	In this paper, we investigate this convexity issue for human computation.
180	Crowdsourcing Complex Workflows under Budget Constraints	Long Tran-Thanh, Trung Dong Huynh, Avi Rosenfeld, Sarvapali D. Ramchurn, Nicholas R. Jennings	We propose Budgeteer, an algorithm to solve this problem under a budget constraint.
181	Efficient Task Sub-Delegation for Crowdsourcing	Han Yu, Chunyan Miao, Zhiqi Shen, Cyril Leung, Yiqiang Chen, Qiang Yang	In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap.
182	When Suboptimal Rules	Avshalom Elmalech, David Sarne, Avi Rosenfeld, Eden Shalom Erez	This paper represents a paradigm shift in what advice agents should provide people.
183	Providing Arguments in Discussions Based on the Prediction of Human Argumentative Behavior	Ariel Rosenfeld, Sarit Kraus	Through extensive human studies with over 200 human subjects, we show that people’s satisfaction from the PRH agent is significantly higher than from other agents that propose arguments based on Argumentation Theory, predict arguments without the heuristics or only the heuristics.
184	Predicting Emotion Perception Across Domains: A Study of Singing and Speaking	Biqiao Zhang, Emily Mower Provost, Robert Swedberg, Georg Essl	In this paper, we investigate acoustic and visual features that are relevant to emotion perception in the domains of singing and speaking.
185	Learning to Manipulate Unknown Objects in Clutter by Reinforcement	Abdeslam Boularias, James Andrew Bagnell, Anthony Stentz	We present a fully autonomous robotic system for grasping objects in dense clutter.
186	Bayesian Active Learning-Based Robot Tutor for Children’s Word-Reading Skills	Goren Gordon, Cynthia Breazeal	Bayesian Active Learning-Based Robot Tutor for Children’s Word-Reading Skills
187	RANSAC versus CS-RANSAC	Geun Sik Jo, Kee-Sung Lee, Devy Chandra, Chol-Hee Jang, Myung-Hyun Ga	CS-RANSAC algorithm in this paper converts RANSAC algorithm into two-layers.
188	Game-Theoretic Approach for Non-Cooperative Planning	Jaume Jordán, Eva Onaindia	In this paper, we present a game-theoretic approach to non-cooperative planning that helps predict before execution what plan schedules agents will adopt so that the set of strategies of all agents constitute a Nash equilibrium.
189	Pattern-Based Variant-Best-Neighbors Respiratory Motion Prediction Using Orthogonal Polynomials Approximation	KinMing Kam, Shouyi Wang, Stephen R. Bowen, Wanpracha Chaovalitwongse	In this paper, we propose a novel respiratory motion prediction framework which integrates four key components: a personalized monitoring window generator, an orthogonal polynomial approximation-based pattern library builder, a variant best neighbor pattern searcher, and a statistical prediction decision maker.
190	Toward Mobile Robots Reasoning Like Humans	Jean H Oh, Arne Suppé, Felix Duvallet, Abdeslam Boularias, Luis Navarro-Serment, Martial Hebert, Anthony Stentz, Jerry Vinokurov, Oscar Romero, Christian Lebiere, Robert Dean	This paper describes recent developments using this architecture on a fielded mobile robot platform operating in unknown urban environments.
191	Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS	Carlotta Schatten, Ruth Janning, Lars Schmidt-Thieme	In this paper we show how we adapted a Matrix Factorization based performance predictor and a score based policy for task sequencing to be integrated in a commercial ITS with over 2000 tasks on 20 topics.
192	Going Beyond Literal Command-Based Instructions: Extending Robotic Natural Language Interaction Capabilities	Tom Williams, Gordon Briggs, Bradley Oosterveld, Matthias Scheutz	In this paper, we propose novel mechanisms for inferring in-tentions from utterances and generating clarification requests that will allow robots to cope with a much wider range of task-based natural language interactions.
193	CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot	Shiqi Zhang, Peter Stone	In particular, in this paper we consider their needs to i) reason with commonsense knowledge, ii) model their nondeterministic action outcomes and partial observability, and iii) plan toward maximizing long-term rewards.
194	Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing	Dawei Zhou, Jiebo Luo, Vincent M.B. Silenzio, Yun Zhou, Jile Hu, Glenn Currier, Henry Kautz	In this study, we investigate how users’ online social activities and physiological signals detected through ubiquitous sensors can be utilized in realistic scenarios for monitoring their mental health states.
195	Ontology Module Extraction via Datalog Reasoning	Ana Armas Romero, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks	In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog.
196	Tractable Interval Temporal Propositional and Description Logics	Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael Zakharyaschev	We design a tractable Horn fragment of the Halpern-Shoham temporal logic and extend it to interval-based temporal description logics, instance checking in which is P-complete for both combined and data complexity.
197	Action Language BC+: Preliminary Report	Joseph Babb, Joohyung Lee	We propose a new action language called BC+, which closes the gap between action languages and the modern ASP language.
198	LARS: A Logic-Based Framework for Analyzing Reasoning over Streams	Harald Beck, Minh Dao-Tran, Thomas Eiter, Michael Fink	We present LARS, a Logic-based framework for Analyzing Reasoning over Streams, i.e., a rule-based formalism with a novel window operator providing a flexible mechanism to represent views on streaming data.
199	Partial Meet Revision and Contraction in Logic Programs	Sebastian Binnewies, Zhiqiang Zhuang, Kewen Wang	In this paper, we bridge the gap between semantic and syntactic techniques by adapting the idea of a partial meet construction from classical belief change.
200	Pearl’s Causality in a Logical Setting	Alexander Bochman, Vladimir Lifschitz	It will be shown that, under this representation, the nonmonotonic semantics of the causal calculus describes precisely the solutions of the structural equations (the causal worlds of the causal model), while the causal logic from Bochman (2004) is adequate for describing the behavior of causal models under interventions (forming submodels).
201	Grounded Fixpoints	Bart Bogaerts, Joost Vennekens, Marc Denecker	In this paper, we add a new type of fixpoint to AFT: a grounded fixpoint of lattice operator O : L → L is defined as a lattice element x ∈ L such that O(x) = x and for all v ∈ L such that O(v ∧ x) ≤ v, it holds that x ≤ v. On the algebraical level, we show that all grounded fixpoints are minimal fixpoints approximated by the well-founded fixpoint and that all stable fixpoints are grounded.
202	Solving and Explaining Analogy Questions Using Semantic Networks	Adrian Boteanu, Sonia Chernova	The approach presented in this work focuses on obtaining precise interpretations of analogies.
203	asprin: Customizing Answer Set Preferences without a Headache	Gerhard Brewka, James Delgrande, Javier Romero, Torsten Schaub	In this paper we describe asprin, a general, flexible, and extensible framework for handling preferences among the stable models of a logic program.
204	Exploiting Parallelism for Hard Problems in Abstract Argumentation	Federico Cerutti, Ilias Tachmazidis, Mauro Vallati, Sotirios Batsakis, Massimiliano Giacomin, Grigoris Antoniou	In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation.
205	A Syntax-Independent Approach to Forgetting in Disjunctive Logic Programs	James Delgrande, Kewen Wang	In this paper, we present an approach to forgetting in disjunctive logic programs, where forgetting an atom from a program amounts to a reduction in the signature of that program.
206	Towards Tractable and Practical ABox Abduction over Inconsistent Description Logic Ontologies	Jianfeng Du, Kewen Wang, Yi-Dong Shen	Hence we propose to use preference information to reduce the number of explanations to be computed.
207	On Computing Explanations in Argumentation	Xiuyi Fan, Francesca Toni	In this work, we propose a new argumentation semantics, related admissibility, designed for giving explanations to arguments in both Abstract Argumentation and Assumption-based Argumentation.
208	Parallelized Hitting Set Computation for Model-Based Diagnosis	Dietmar Jannach, Thomas Schmitz, Kostyantyn Shchekotykhin	Since many of today’s computing devices have multi-core CPU architectures, we propose techniques to parallelize the construction of the tree to better utilize the computing resources without losing any diagnoses.
209	Splitting a Logic Program Revisited	Jianmin Ji, Hai Wan, Ziwei Huo, Zhenfeng Yuan	In this paper, we extend Lifschitz and Turner’s splitting set theorem to allow the program to be split by an arbitrary set of atoms, while some new atoms may be introduced in the process.
210	On Elementary Loops and Proper Loops for Disjunctive Logic Programs	Jianmin Ji, Hai Wan, Peng Xiao	This paper proposes an alternative definition of elementary loops and extends the notion of proper loops for disjunctive logic programs.
211	XPath for DL Ontologies	Egor V. Kostylev, Juan L. Reutter, Domagoj Vrgoc	In this paper we make a step towards coupling knowledge bases and graph databases by studying how to answer powerful XPath-style queries over simple DLs like DL-Lite and EL.
212	An Abstract View on Modularity in Knowledge Representation	Yuliya Lierler, Miroslaw Truszczynski	We introduce model-based modular systems, an abstract framework for modular knowledge representation formalisms, similar in scope to multi-context systems but employing a simpler information-flow mechanism.
213	Learning Partial Lexicographic Preference Trees over Combinatorial Domains	Xudong Liu, Miroslaw Truszczynski	We introduce partial lexicographic preference trees (PLPtrees) as a formalism for compact representations of preferences over combinatorial domains.
214	From Classical to Consistent Query Answering under Existential Rules	Thomas Lukasiewicz, Maria Vanina Martinez, Andreas Pieris, Gerardo I Simari	The goal of the current work is to perform an in-depth analysis of the complexity of consistent query answering under the main decidable classes of existential rules enriched with negative constraints.
215	Belief Revision with General Epistemic States	Hua Meng, Hui Kou, Sanjiang Li	To provide a semantical characterisation of GEPs, we introduce a mathematical structure called belief algebra, which is in essence a certain binary relation defined on the power set of worlds.We then establish a 1-1 correspondence between GEPs and belief algebras, and show that total preorders on worlds are special cases of belief algebras.
216	Incremental Update of Datalog Materialisation: the Backward/Forward Algorithm	Boris Motik, Yavor Nenov, Robert Edgar Felix Piro, Ian Horrocks	As a possible remedy, we propose a novel Backward/Forward (B/F) algorithm that tries to reduce the amount of work by a combination of backward and forward chaining.
217	Logic Programming in Assumption-Based Argumentation Revisited – Semantics and Graphical Representation	Claudia Schulz, Francesca Toni	Logic Programming in Assumption-Based Argumentation Revisited – Semantics and Graphical Representation
218	Minimizing User Involvement for Accurate Ontology Matching Problems	Anika Schumann, Freddy Lecue	This paper addresses the problem of determining accurate and complete matching of ontologies given some common descriptions and their pre-determined high level alignments.
219	Projection in the Epistemic Situation Calculus with Belief Conditionals	Christoph Schwering, Gerhard Lakemeyer	In this paper, we show how regression can be extended to reduce beliefs about the future to initial beliefs in the presence of belief conditionals.
220	Belief Revision Games	Nicolas Schwind, Katsumi Inoue, Gauvain Bourgne, Sébastien Konieczny, Pierre Marquis	We provide a general definition for such games where each agent has her own revision policy, and show that the belief sequences of agents can always be finitely characterized.
221	Interactive Query-Based Debugging of ASP Programs	Kostyantyn Shchekotykhin	In this paper we present an interactive query-based ASP debugging method which extends previous approaches and finds the preferred explanation by means of observations.
222	Exploring the KD45 Property of a Kripke Model After the Execution of an Action Sequence	Tran Cao Son, Enrico Pontelli, Chitta Baral, Gregory Gelfond	The paper proposes a condition for preserving the KD45 property of a Kripke model when a sequence of update models is applied to it.
223	Answering Conjunctive Queries over EL Knowledge Bases with Transitive and Reflexive Roles	Giorgio Stefanoni, Boris Motik	In this paper we complete the complexity landscape of CQ answering for several important cases.
224	How Many Diagnoses Do We Need?	Roni Tzvi Stern, Meir Kalech, Shelly Rogov, Alexander Feldman	We propose a way to aggregate an arbitrarily large set of diagnoses to return an estimate of the likelihood of a given component to be faulty.
225	The Relative Expressiveness of Abstract Argumentation and Logic Programming	Hannes Strass	We analyze the relative expressiveness of the two-valued semantics of abstract argumentation frameworks, normal logic programs and abstract dialectical frameworks.
226	A Comparison of Qualitative and Metric Spatial Relation Models for Scene Understanding	Akshaya Thippur, Chris Burbridge, Lars Kunze, Marina Alberti, John Folkesson, Patric Jensfelt, Nick Hawes	In this paper, we present techniques to model and infer object labels in real scenes based on a variety of spatial relations — geometric features which capture how objects co-occur — and compare their efficacy in the context of augmenting perception based object classification in real-world table-top scenes.
227	On the Role of Canonicity in Knowledge Compilation	Guy Van den Broeck, Adnan Darwiche	We resolve this open question in this paper and consider some of its theoretical and practical implications.
228	Knowledge Forgetting in Circumscription: A Preliminary Report	Yisong Wang, Kewen Wang, Zhe Wang, Zhiqiang Zhuang	A sound and complete algorithm for the forgetting is developed and an analysis of computational complexity is given.
229	Instance-Driven Ontology Evolution in DL-Lite	Zhe Wang, Kewen Wang, Zhiqiang Zhuang, Guilin Qi	In this paper we introduce a model-theoretic approach to such a contraction problem by using an alternative semantic characterisation of DL-Lite TBoxes.
230	Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base	Fei Wu, Jun Song, Yi Yang, Xi Li, Zhongfei Zhang, Yueting Zhuang	Therefore, in this paper we introduces Path-Ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it ‘structured embedding via pairwise relation and long-range interactions’ (referred to as SePLi).
231	A Logic for Reasoning About Game Strategies	Dongmo Zhang, Michael Thielscher	This paper introduces a modal logic for reasoning about game strategies.
232	Existential Rule Languages with Finite Chase: Complexity and Expressiveness	Heng Zhang, Yan Zhang, Jia-Huai You	In this work, we propose a novel approach for classifying the rule languages with finite chase.
233	Variational Inference for Nonparametric Bayesian Quantile Regression	Sachinthaka Abeywardana, Fabio Ramos	In this work we present a non-parametric method of inferring quantiles and derive a novel Variational Bayesian (VB) approximation to the marginal likelihood, leading to an elegant Expectation Maximisation algorithm for learning the model.
234	Sample-Targeted Clinical Trial Adaptation	Ognjen Arandjelovic	We propose a novel method for achieving this.
235	A Sparse Combined Regression-Classification Formulation for Learning a Physiological Alternative to Clinical Post-Traumatic Stress Disorder Scores	Sarah Marie Brown, Andrea Webb, Rami Mangoubi, Jennifer Dy	In pursuit of an automated physiology-based objective diagnostic method, we propose a learning formulation that integrates the description of the experimental data and expert knowledge on desirable properties of a physiological diagnostic score.
236	Marginalized Denoising for Link Prediction and Multi-Label Learning	Zheng Chen, Minmin Chen, Kilian Weinberger, Weixiong Zhang	We develop a novel algorithm to solve these two problems jointly under a unified framework, which helps reduce the impact of graph noise and benefits both tasks individually.
237	Structured Sparsity with Group-Graph Regularization	Xin-Yu Dai, Jian-Bing Zhang, Shu-Jian Huang, Jia-Jun Chen, Zhi-Hua Zhou	In this paper, we propose a g2-regularization that takes group and graph sparsity into joint consideration, and present an effective approach for its optimization.
238	Content-Aware Point of Interest Recommendation on Location-Based Social Networks	Huiji Gao, Jiliang Tang, Xia Hu, Huan Liu	In this work, we study the content information on LBSNs w.r.t. POI properties, user interests, and sentiment indications.
239	Constructing Models of User and Task Characteristics from Eye Gaze Data for User-Adaptive Information Highlighting	Matthew Junghyun Gingerich, Cristina Conati	In this paper, we investigate the accuracy of predicting visualization tasks, user performance on tasks, and user traits from gaze data.
240	Automatic Assessment of OCR Quality in Historical Documents	Anshul Gupta, Ricardo Gutierrez-Osuna, Matthew Christy, Boris Capitanu, Loretta Auvil, Liz Grumbach, Richard Furuta, Laura Mandell	This paper presents an iterative classification algorithm to automatically label BBs (i.e., as text or noise) based on their spatial distribution and geometry.
241	PD Disease State Assessment in Naturalistic Environments Using Deep Learning	Nils Yannick Hammerla, James Fisher, Peter Andras, Lynn Rochester, Richard Walker, Thomas Ploetz	In this work we propose an assessment system that abides practical usability constraints and applies deep learning to differentiate disease state in data collected in naturalistic settings.
242	Identifying At-Risk Students in Massive Open Online Courses	Jiazhen He, James Bailey, Benjamin I. P. Rubinstein, Rui Zhang	In this paper, we explore the accurate early identification of students who are at risk of not completing courses.
243	Exploiting Determinism to Scale Relational Inference	Mohamed Hamza Ibrahim, Christopher Pal, Gilles Pesant	In this paper we introduce Preference Relaxation (PR), a two-stage strategy that uses the determinism present in the underlying model to improve the scalability of relational inference.
244	Scalable and Interpretable Data Representation for High-Dimensional, Complex Data	Been Kim, Kayur Patel, Afshin Rostamizadeh, Julie Shah	In this paper, we quantitatively and qualitatively evaluate an efficient, accurate and scalable feature-compression method using latent Dirichlet allocation for discrete data.
245	Learning to Uncover Deep Musical Structure	Phillip Kirlin, David Jensen	We present a new probabilistic model of this variety of music analysis, details of how the parameters of the model can be learned from a corpus, an algorithm for deriving the most probable analysis for a given piece of music, and both quantitative and human-based evaluations of the algorithm’s performance.
246	Nonstationary Gaussian Process Regression for Evaluating Repeated Clinical Laboratory Tests	Thomas A. Lasko	In this paper we analyze hundreds of observation sequences of four different clinical laboratory tests to provide principled, data-driven estimates of undersampling and oversampling, and to assess whether the sampling adapts to changing volatility of the latent function.
247	Tensor-Based Learning for Predicting Stock Movements	Qing Li, LiLing Jiang, Ping Li, Hsinchun Chen	In this article, we model the multi-faceted investors’ information and their intrinsic links with tensors.
248	Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching	Zhe Lim, Benjamin Rubinstein	We propose a more fine-grained approach that focuses on correspondences between the values of attributes across data sources.
249	A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis	Zitao Liu, Milos Hauskrecht	In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs’ spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting.
250	Generalized Singular Value Thresholding	Canyi Lu, Changbo Zhu, Chunyan Xu, Shuicheng Yan, Zhouchen Lin	We prove that GSVT can be obtained by performing the proximal operator of g on the singular values since Proxg(.)
251	Lazier Than Lazy Greedy	Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi, Jan Vondrak, Andreas Krause	In this paper, we develop the first linear-time algorithm for maximizing a general monotone submodular function subject to a cardinality constraint.
252	Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series	George D. Montanez, Saeed Amizadeh, Nikolay Laptev	Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task.
253	Algorithm Selection via Ranking	Richard Jayadi Oentaryo, Stephanus Daniel Handoko, Hoong Chuin Lau	In this paper, we present a new approach that provides a more natural treatment of algorithm selection and portfolio construction as a ranking task.
254	Propagating Ranking Functions on a Graph: Algorithms and Applications	Buyue Qian, Xiang Wang, Ian Davidson	To address this, we propose to construct a graph where each node corresponds to a retrieval task, and then propagate ranking functions on the graph.
255	On Vectorization of Deep Convolutional Neural Networks for Vision Tasks	Jimmy SJ. Ren, Li Xu	In this paper, we studied the vectorization process of key building blocks in deep CNNs, in order to better understand and facilitate parallel implementation.
256	Learning Hybrid Models with Guarded Transitions	Pedro Santana, Spencer Lane, Eric Timmons, Brian Williams, Carlos Forster	In this paper, we present a novel algorithm capable of performing unsupervised learning of guarded Probabilistic Hybrid Automata (PHA) models, which extends prior work by allowing stochastic discrete mode transitions in a hybrid system to have a functional dependence on its continuous state.
257	Transaction Costs-Aware Portfolio Optimization via Fast Lowner-John Ellipsoid Approximation	Weiwei Shen, Jun Wang	In this paper, we develop an approximate dynamic programing method of synergistically combining the Lowner-John ellipsoid approximation with conventional value function iteration to quantify the associated optimal trading policy.
258	Coupled Interdependent Attribute Analysis on Mixed Data	Can Wang, Chi-Hung Chi, Wei Zhou, Raymond Wong	This paper proposes the coupled heterogeneous attribute analysis to capture the interdependence among mixed data by addressing coupling context and coupling weights in unsupervised learning.
259	Exploring Social Context for Topic Identification in Short and Noisy Texts	Xin Wang, Ying Wang, Wanli Zuo, Guoyong Cai	In particular, we present a mathematical optimization formulation that incorporates the preference consistency and social contagion theories into a supervised learning method, and conduct feature selection to tackle short and noisy texts in social media, which result in a Sociological framework for Topic Identification (STI).
260	Modeling Status Theory in Trust Prediction	Ying Wang, Xin Wang, Jiliang Tang, Wanli Zuo, Guoyong Cai	In this paper, we investigate how to exploit social status in trust prediction by modeling status theory.
261	Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification	Lan Wei, Yonghong Tian, Yaowei Wang, Tiejun Huang	In order to improve the robustness of gait-based person re-identification on such multi-covariate conditions, a novel Swiss-system based cascade ranking model is proposed in this paper.
262	An SVD and Derivative Kernel Approach to Learning from Geometric Data	Eric Wong, J. Zico Kolter	Since many physical simulations based upon geometric data produce derivatives of the output quantity with respect to the input positions, we derive an approach that incorporates derivative information into our kernel learning.
263	Mining User Interests from Personal Photos	Pengtao Xie, Yulong Pei, Yuan Xie, Eric Xing	In this paper, we study the problem of user interests mining from personal photos.
264	Integrating Image Clustering and Codebook Learning	Pengtao Xie, Eric P. Xing	Traditionally, these two processes are conducted separately and their correlation is generally ignored.In this paper, we propose a Double Layer Gaussian Mixture Model (DLGMM) to simultaneously perform image clustering and codebook learning.
265	Stable Feature Selection from Brain sMRI	Bo Xin, Lingjing Hu, Yizhou Wang, Wen Gao	In this paper, we explore a nonnegative generalized fused lasso model for stable feature selection in the diagnosis of Alzheimer’s disease.
266	Forecasting Collector Road Speeds Under High Percentage of Missing Data	Xin Xin, Chunwei Lu, Yashen Wang, Heyan Huang	Aiming at solving this problem, we propose a multi-view road speed prediction framework.
267	Large-Margin Multi-Label Causal Feature Learning	Chang Xu, Dacheng Tao, Chao Xu	Since the original features are a disorderly mixture of the properties originating from different labels, it is intuitive to factorize these raw features to clearly represent each individual label and its causality relationship.Following the large-margin principle, we propose an effective approach to discover the causal features of multiple labels, thus revealing the causality between labels from the perspective of feature.
268	Exploiting Task-Feature Co-Clusters in Multi-Task Learning	Linli Xu, Aiqing Huang, Jianhui Chen, Enhong Chen	This paper presents a multi-task learning approach by modeling the task-feature relationships.
269	Temporally Adaptive Restricted Boltzmann Machine for Background Modeling	Linli Xu, Yitan Li, Yubo Wang, Enhong Chen	A novel approach is proposed based on the Restricted Boltzmann Machine (RBM) while exploiting the temporal nature of the problem.
270	On Machine Learning towards Predictive Sales Pipeline Analytics	Junchi Yan, Chao Zhang, Hongyuan Zha, Min Gong, Changhua Sun, Jin Huang, Stephen Chu, Xiaokang Yang	In contrast to using subjective human rating, we propose a modern machine learning paradigm to estimate the win-propensity of sales leads over time.
271	Bayesian Approach to Modeling and Detecting Communities in Signed Network	Bo Yang, Xuehua Zhao, Xueyan Liu	To address them, we propose a generative Bayesian approach, in which 1) a signed stochastic blockmodel is proposed to characterize the community structure in context of signed networks, by means of explicitly formulating the distributions of both density and frustration of signed links from a stochastic perspective, and 2) a model learning algorithm is proposed by theoretically deriving a variational Bayes EM for parameter estimation and a variation based approximate evidence for model selection.
272	Colorization by Patch-Based Local Low-Rank Matrix Completion	Quanming Yao, T. Kwok James	In this paper, we propose a patch-based approach that divides the image into patches and then imposes a low-rank structure only on groups of similar patches.
273	Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease	Shandian Zhe, Zenglin Xu, Yuan Qi, Peng Yu	To harness their potential benefits for each other, we propose a new sparse Bayesian approach to jointly carry out the two important and related tasks.
274	A Closed Form Solution to Multi-View Low-Rank Regression	Shuai Zheng, Xiao Cai, Chris Ding, Feiping Nie, Heng Huang	In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on multi-view regression model.
275	A Nonconvex Relaxation Approach for Rank Minimization Problems	Xiaowei Zhong, Linli Xu, Yitan Li, Zhiyuan Liu, Enhong Chen	In this paper, we propose an Iterative Shrinkage-Thresholding and Reweighted Algorithm (ISTRA) to solve rank minimization problems using the nonconvex weighted nuclear norm as a low rank regularizer.
276	An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types	Stefano Vittorino Albrecht, Jacob William Crandall, Subramanian Ramamoorthy	In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions.
277	Scalable Planning and Learning for Multiagent POMDPs	Christopher Amato, Frans A Oliehoek	To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings.
278	Multi-Agent Pathfinding as a Combinatorial Auction	Ofra Amir, Guni Sharon, Roni Stern	This paper proposes a mapping between multi-agent pathfinding (MAPF) and combinatorial auctions (CAs).
279	Cooperating with Unknown Teammates in Complex Domains: A Robot Soccer Case Study of Ad Hoc Teamwork	Samuel Barrett, Peter Stone	To handle these complex scenarios, we introduce a new algorithm, PLASTIC–Policy, that builds on an existing ad hoc teamwork approach.
280	Cognitive Social Learners: An Architecture for Modeling Normative Behavior	Rahmatollah Beheshti, Awrad Mohammed Ali, Gita Reese Sukthankar	In this paper, we introduce a new normative multi-agent architecture, Cognitive Social Learners (CSL), that models bottom-up norm emergence through a social learning mechanism, while using BDI (Belief/Desire/Intention) reasoning to handle adoption and compliance.
281	Multi-Agent Path Finding on Strongly Biconnected Digraphs	Adi Botea, Pavel Surynek	We present a polynomial-time algorithm for this class of problems, and analyze its complexity.
282	Verification of Relational Multiagent Systems with Data Types	Diego Calvanese, Giorgio Delzanno, Marco Montali	We study the extension of relational multiagent systems (RMASs), where agents manipulate full-fledged relational databases, with data types and facets equipped with domain-specific, rigid relations (such as total orders).
283	Verifying and Synthesising Multi-Agent Systems against One-Goal Strategy Logic Specifications	Petr Čermák, Alessio Lomuscio, Aniello Murano	In this paper we put forward an automata-based methodology for verifying and synthesising multi-agent systems against specifications given in SL[1G].
284	Elections with Few Voters: Candidate Control Can Be Easy	Jiehua Chen, Piotr Faliszewski, Rolf Niedermeier, Nimrod Talmon	We study the computational complexity of candidate control in elections with few voters (that is, we take the number of voters as a parameter).
285	Cupid: Commitments in Relational Algebra	Amit Chopra, Munindar Singh	We propose Cupid, a language for specifying commitments that supports their information-centric aspects, and offers crucial benefits.
286	Automated Analysis of Commitment Protocols Using Probabilistic Model Checking	Akın Günay, Song Songzheng, Yang Liu, Jie Zhang	Automated Analysis of Commitment Protocols Using Probabilistic Model Checking
287	Fast Convention Formation in Dynamic Networks Using Topological Knowledge	Mohammad Rashedul Hasan, Anita Raja, Ana Bazzan	In this paper, we design a distributed mechanism that is able to create a social convention within a large convention space for multiagent systems (MAS) operating on various topologies.
288	Distributed Multiplicative Weights Methods for DCOP	Daisuke Hatano, Yuichi Yoshida	We introduce a new framework for solving distributed constraint optimization problems that extend the domain of each variable into a simplex.We propose two methods for searching the extended domain for good assignments.The first one relaxes the problem using linear programming, finds the optimum LP solution, and rounds it to an assignment.The second one plays a cost-minimization game, finds a certain kind of equilibrium, and rounds it to an assignment.Both methods are realized by performing the multiplicative weights method in a distributed manner.We experimentally demonstrate that our methods have good scalability,and in particular, the second method outperforms existing algorithms in terms of solution quality and efficiency.
289	A Counter Abstraction Technique for the Verification of Robot Swarms	Panagiotis Kouvaros, Alessio Lomuscio	We relax some of the significant restrictions assumed in the literature and present a counter abstraction approach that enable us to verify a potentially much smaller abstract model when checking a formula on a swarm of any size.
290	Generalization Analysis for Game-Theoretic Machine Learning	Haifang Li, Fei Tian, Wei Chen, Tao Qin, Zhi-Ming Ma, Tie-Yan Liu	Generalization Analysis for Game-Theoretic Machine Learning
291	SCRAM: Scalable Collision-avoiding Role Assignment with Minimal-Makespan for Formational Positioning	Patrick MacAlpine, Eric Price, Peter Stone	We present scaleable (computable in polynomial time) role assignment algorithms that we classify as being SCRAM (Scalable Collision-avoiding Role Assignment with Minimal-makespan).
292	Plurality Voting Under Uncertainty	Reshef Meir	This paper extends the model in multiple directions, considering voters with different uncertainty levels, simultaneous strategic decisions, and a more permissive notion of best-response.
293	Multi-Robot Auctions for Allocation of Tasks with Temporal Constraints	Ernesto Nunes, Maria Gini	We propose an auction algorithm to allocate tasks that have temporal constraints to cooperative robots.
294	Distributing Coalition Value Calculations to Coalition Members	Luke Riley, Katie Atkinson, Paul E. Dunne, Terry R. Payne	We introduce DCG, a novel algorithm that distributes the calculations of coalition utility values across a community of agents, such that: (i) no inter-agent communication is required; (ii) the coalition value calculations are (approximately) equally partitioned into shares, one for each agent; (iii) the utility value is calculated only once for each coalition, thus redundant calculations are eliminated; (iv) there is an equal number of operations for agents with equal sized shares; and (v) an agent is only allocated those coalitions in which it is a potential member.
295	Fully Proportional Representation with Approval Ballots: Approximating the MaxCover Problem with Bounded Frequencies in FPT Time	Piotr Krzysztof Skowron, Piotr Faliszewski	This problem is equivalent to the well-known NP-complete MaxCover problem (i.e., a version of the SetCover problem where we aim to cover as many elements as possible) and, so, the best polynomial-time approximation algorithm for it has approximation ratio 1 – 1/e.
296	Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation	Piotr Krzysztof Skowron, Piotr Faliszewski, Jerome Lang	Our goal is to pick a set of K items that maximize the total derived utility of all the agents (i.e., in our example we are to pick K movies that we put on the plane’s entertainment system).
297	Solving Games with Functional Regret Estimation	Kevin Waugh, Dustin Morrill, James Andrew Bagnell, Michael Bowling	We propose a novel online learning method for minimizing regret in large extensive-form games.
298	Learning Word Representations from Relational Graphs	Danushka Bollegala, Takanori Maehara, Yuichi Yoshida, Ken-ichi Kawarabayashi	Motivated by this close connection between attributes and relations, given a relational graph in which words are inter-connected via numerous semantic relations, we propose a method to learn a latent representation for the individual words.
299	Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarization	Ziqiang Cao, Furu Wei, Li Dong, Sujian Li, Ming Zhou	We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document summarization.
300	Refer-to-as Relations as Semantic Knowledge	Song Feng, Sujith Ravi, Ravi Kumar, Polina Kuznetsova, Wei Liu, Alexander C. Berg, Tamara L. Berg, Yejin Choi	Our contributions include a new labeleddata set, the inference and optimization approach, andthe computed mappings and similarities.
301	Surveyor: A System for Generating Coherent Survey Articles for Scientific Topics	Rahul Jha, Reed Coke, Dragomir Radev	We introduce an extractive summarization algorithm that combines a content model with a discourse model to generate coherent and readable summaries of scientific topics using text from scientific articles relevant to the topic.
302	Automatically Creating a Large Number of New Bilingual Dictionaries	Khang Nhut Lam, Feras Al Tarouti, Jugal Kalita	This paper proposes approaches to automatically createa large number of new bilingual dictionaries for low resource languages, especially resource-poor and endangered languages, from a single input bilingual dictionary.
303	Learning Entity and Relation Embeddings for Knowledge Graph Completion	Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu	In this paper, we consider the approach of knowledge graph embeddings.
304	Sense-Aaware Semantic Analysis: A Multi-Prototype Word Representation Model Using Wikipedia	Zhaohui Wu, C. Lee Giles	We present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy.
305	Phrase Type Sensitive Tensor Indexing Model for Semantic Composition	Yu Zhao, Zhiyuan Liu, Maosong Sun	In this paper, we propose to synchronously learn the representations of individual words and extracted high-frequency phrases.
306	Predicting Peer-to-Peer Loan Rates Using Bayesian Non-Linear Regression	Zsolt Bitvai, Trevor Cohn	Here we consider modelling market rates, developing a non-linear Gaussian Process regression method which incorporates both structured data and unstructured text from the loan application.
307	A Novel Neural Topic Model and Its Supervised Extension	Ziqiang Cao, Sujian Li, Yang Liu, Wenjie Li, Heng Ji	Based on this, we propose a novel neural topic model (NTM) where the representation of words and documents are efficiently and naturally combined into a uniform framework.
308	Unsupervised Word Sense Disambiguation Using Markov Random Field and Dependency Parser	Devendra Singh Chaplot, Pushpak Bhattacharyya, Ashwin Paranjape	Using two basic ideas, sense dependency and selective dependency, we model the WSD problem as a Maximum A Posteriori (MAP) Inference Query on a Markov Random Field (MRF) built using WordNet and Link Parser or Stanford Parser.
309	Dataless Text Classification with Descriptive LDA	Xingyuan Chen, Yunqing Xia, Peng Jin, John Carroll	In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents.
310	Topic Segmentation with an Ordering-Based Topic Model	Lan Du, John K. Pate, Mark Johnson	In this paper we present a simple topic model that uses generalised Mallows models and incomplete topic orderings to incorporate this ordering regularity into the probabilistic generative process of the new model.
311	A Stratified Strategy for Efficient Kernel-Based Learning	Simone Filice, Danilo Croce, Roberto Basili	When the model size grows with the complexity of the task, such approaches are so computationally demanding that the adoption of comprehensive models is not always viable.In this paper, a general framework aimed at minimizing this problem is proposed: multiple classifiers are stratified and dynamically invoked according to increasing levels of complexity corresponding to incrementally more expressive representation spaces.Computationally expensive inferences are thus adopted only when the classification at lower levels is too uncertain over an individual instance.
312	Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning	Dan Garrette, Chris Dyer, Jason Baldridge, Noah A. Smith	We present a Bayesian formulation for CCG parser induction that assumes only supervision in the form of an incomplete tag dictionary mapping some word types to sets of potential categories.
313	Unsupervised Phrasal Near-Synonym Generation from Text Corpora	Dishan Gupta, Jaime Carbonell, Anatole Gershman, Steve Klein, David Miller	This paper presents an unsupervised corpus-based conditional model: Near-Synonym System (NeSS) for finding phrasal synonyms and near synonyms that requires only a large monolingual corpus.
314	Local Context Sparse Coding	Seungyeon Kim, Joonseok Lee, Guy Lebanon, Haesun Park	This paper presents local context sparse coding (LCSC), a non-probabilistic topic model that effectively handles large feature spaces using sparse coding.
315	Recurrent Convolutional Neural Networks for Text Classification	Siwei Lai, Liheng Xu, Kang Liu, Jun Zhao	In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features.
316	Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures	Emmanuel Lassalle, Pascal Denis	This paper introduces a new structured model for learning anaphoricity detection and coreference resolution in a joint fashion.
317	Fast and Accurate Prediction of Sentence Specificity	Junyi Jessy Li, Ani Nenkova	In this paper we present a practical system for predicting sentence specificity which exploits only features that require minimum processing and is trained in a semi-supervised manner.
318	Learning to Mediate Perceptual Differences in Situated Human-Robot Dialogue	Changsong Liu, Joyce Yue Chai	To overcome this problem, we have developed an optimization based approach that allows the robot to detect andadapt to perceptual differences.
319	Contrastive Unsupervised Word Alignment with Non-Local Features	Yang Liu, Maosong Sun	We propose a contrastive approach that aims to differentiate observed training examples from noises.
320	Never-Ending Learning	Tom M. Mitchell, William Cohen, Estevam Hruschka, Partha Talukdar, Justin Betteridge, Andrew Carlson, Bhavana Dalvi Mishra, Matthew Gardner, Bryan Kisiel, Jayant Krishnamurthy, Ni Lao, Kathryn Mazaitis, Thahir Mohamed, Ndapa Nakashole, Emmanouil Antonios Platanios, Alan Ritter, Mehdi Samadi, Burr Settles, Richard Wang, Derry Wijaya, Abhinav Gupta, Xinlei Chen, Abulhair Saparov, Malcolm Greaves, Joel Welling	We propose a never-ending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans.
321	The Utility of Text: The Case of Amicus Briefs and the Supreme Court	Yanchuan Sim, Bryan R Routledge, Noah A Smith	We explore the idea that authoring a piece of text is an act of maximizing one’s expected utility.To make this idea concrete, we consider the societally important decisions of the Supreme Court of the United States.Extensive past work in quantitative political science provides a framework for empirically modeling the decisions of justices and how they relate to text.We incorporate into such a model texts authored by amici curiae (“friends of the court” separate from the litigants) who seek to weigh in on the decision, then explicitly model their goals in a random utility model.We demonstrate the benefits of this approach in improved vote prediction and the ability to perform counterfactual analysis.
322	A Family of Latent Variable Convex Relaxations for IBM Model 2	Andrei Arsene Simion, Michael Collins, Cliff Stein	As proof of concept, we study a new relaxation of IBM Model 2 which is simpler than the previous algorithm: the new relaxation relies on the use of nothing more than a multinomial EM algorithm, does not require the tuning of a learning rate, and has some favorable comparisons to IBM Model 2 in terms of F-Measure.
323	Online Bayesian Models for Personal Analytics in Social Media	Svitlana Volkova, Benjamin Van Durme	We propose various approaches to handling this dynamic data, from traditional batch training and testing, to incremental bootstrapping, and then active learning via crowdsourcing.
324	Microblog Sentiment Classification with Contextual Knowledge Regularization	Fangzhao Wu, Yangqiu Song, Yongfeng Huang	In this paper, we propose to use the microblogs’ contextual knowledge mined from a large amount of unlabeled data to help improve microblog sentiment classification.
325	Learning Greedy Policies for the Easy-First Framework	Jun Xie, Chao Ma, Janardhan Rao Doppa, Prashanth Mannem, Xiaoli Fern, Thomas G. Dietterich, Prasad Tadepalli	We formulate greedy policy learning in the Easy-first approach as a novel non-convex optimization problem and solve it via an efficient Majorization Minimizatoin (MM) algorithm.
326	Jointly Modeling Deep Video and Compositional Text to Bridge Vision and Language in a Unified Framework	Ran Xu, Caiming Xiong, Wei Chen, Jason J Corso	In this paper, we propose a unified framework that jointly models video and the corresponding text sentences.
327	Ordering-Sensitive and Semantic-Aware Topic Modeling	Min Yang, Tianyi Cui, Wenting Tu	In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling.
328	Target-Dependent Churn Classification in Microblogs	Hadi Amiri, Hal Daume III	We consider the problem of classifying micro-posts as churny or non-churny with respect to a given brand.
329	English Light Verb Construction Identification Using Lexical Knowledge	Wei-Te Chen, Claire Bonial, Martha Palmer	This research describes the development of a supervised classifier of English light verb constructions, for example, “take a walk” and “make a speech.”
330	Chinese Common Noun Phrase Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers	Chen Chen, Vincent Ng	In this paper, we propose a generative model for unsupervised Chinese common noun phrase resolution that not only allows easy incorporation of linguistic constraints on coreference but also performs joint resolution and anaphoricity determination.
331	Gazetteer-Independent Toponym Resolution Using Geographic Word Profiles	Grant DeLozier, Jason Baldridge, Loretta London	We address this limitation by modeling the geographic distributions of words over the earth’s surface: we calculate the geographic profile of each word based on local spatial statistics over a set of geo-referenced language models.
332	Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network	Xiao Ding, Ting Liu, Junwen Duan, Jian-Yun Nie	In this paper, we investigate the use of social media data to identify consumption intentions for individuals.
333	Generating Event Causality Hypotheses through Semantic Relations	Chikara Hashimoto, Kentaro Torisawa, Julien Kloetzer, Jong-Hoon Oh	We propose a method of hypothesizing unseen event causalities from known event causalities extracted from the web by the semantic relations between nouns.
334	A Neural Probabilistic Model for Context Based Citation Recommendation	Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, C. Lee Giles	To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers.
335	Extracting Verb Expressions Implying Negative Opinions	Huayi Li, Arjun Mukherjee, Jianfeng Si, Bing Liu	In this paper, we make an attempt to solve this problem.
336	Topical Word Embeddings	Yang Liu, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun	In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both words and their topics.
337	Towards Knowledge-Driven Annotation	Yassine Mrabet, Claire Gardent, Muriel Foulonneau, Elena Simperl, Eric Ras	In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem.
338	Semantic Lexicon Induction from Twitter with Pattern Relatedness and Flexible Term Length	Ashequl Qadir, Pablo N. Mendes, Daniel Gruhl, Neal Lewis	We present a novel semantic lexicon induction approach that is able to learn new vocabulary from social media.
339	Word Segmentation for Chinese Novels	Likun Qiu, Yue Zhang	We investigate a method forautomatically mining common noun entities for eachnovel using information extraction techniques, and usethe resulting entities to improve a state-of-the-art segmentationmodel for the novel.
340	Using Frame Semantics for Knowledge Extraction from Twitter	Anders Søgaard, Barbara Plank, Hector Martinez Alonso	In this paper we address the question whether we can exploit social media to extract new facts, which may at first seem like finding needles in haystacks.
341	Learning to Recommend Quotes for Writing	Jiwei Tan, Xiaojun Wan, Jianguo Xiao	In this paper, we propose and address a novel task of recommending quotes for writing.
342	Extracting Adverse Drug Reactions from Social Media	Andrew Yates, Nazli Goharian, Ophir Frieder	We propose three methods for extracting ADRs from forum posts and tweets, and compare our methods with several existing methods.
343	An Unsupervised Framework of Exploring Events on Twitter: Filtering, Extraction and Categorization	Deyu Zhou, Liangyu Chen, Yulan He	In this paper we propose a general unsupervised framework to explore events from tweets, which consists of a pipeline process of filtering, extraction and categorization.
344	A Probabilistic Covariate Shift Assumption for Domain Adaptation	Tameem Adel, Alexander Wong	We present a domain adaptation algorithm that assumes a relaxed version of covariate shift where the assumption that the labeling functions of the source and target domains are identical holds with a certain probability.
345	Efficient Active Learning of Halfspaces via Query Synthesis	Ibrahim Alabdulmohsin, Xin Gao, Xiangliang Zhang	In this paper, we present an efficient spectral algorithm for membership query synthesis for halfspaces, whose sample complexity is experimentally shown to be near-optimal.
346	Budgeted Prediction with Expert Advice	Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak Turaga	We provide an online learning algorithm for this setting with regret after T prediction rounds bounded by O(sqrt(C log(N)T/B)), where C is the total cost of all experts.
347	Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization	Olov Andersson, Fredrik Heintz, Patrick Doherty	In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints.
348	Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment	Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor	This paper introduces an autonomous framework that uses unsupervised manifold alignment to learn inter-task mappings and effectively transfer samples between different task domains.
349	Aligning Mixed Manifolds	Thomas Boucher, CJ Carey, Sridhar Mahadevan, Melinda Darby Dyar	This paper proposes a novel manifold alignment algorithm, low rank alignment (LRA), that uses a low rank representation (instead of a nearest neighbor graph construction) to embed and align data sets drawn from mixtures of manifolds.
350	Deep Modeling Complex Couplings within Financial Markets	Wei Cao, Liang Hu, Longbing Cao	In this paper, a coupled deep belief network is designed to accommodate the above three types of couplings across financial markets.
351	Structural Learning with Amortized Inference	Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, Dan Roth	We propose AI-DCD, an Amortized Inference framework for Dual Coordinate Descent method, an approximate learning algorithm, that accelerates the training process by exploiting this redundancy of solutions, without compromising the performance of the model.
352	A Convex Formulation for Spectral Shrunk Clustering	Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, Xiaofang Zhou	Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace.
353	Learning Relational Kalman Filtering	Jaesik Choi, Eyal Amir, Tianfang Xu, Albert J. Valocchi	In this paper, we provide a parameter learning algorithm for RKF, and a regrouping algorithm that prevents the degeneration of the relational structure for efficient filtering.
354	Policy Tree: Adaptive Representation for Policy Gradient	Ujjwal Das Gupta, Erik Talvitie, Michael Bowling	This paper introduces the Policy Tree algorithm, which can learn an adaptive representation of policy in the form of a decision tree over different instantiations of a base policy.
355	Collaborative Filtering with Localised Ranking	Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon	With this in mind we propose a class of objective functions which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list.
356	Random Gradient Descent Tree: A Combinatorial Approach for SVM with Outliers	Hu Ding, Jinhui Xu	In this paper, we present a new combinatorial approach, called Random Gradient Descent Tree (or RGD-tree), to explicitly deal with outliers; this results in a new algorithm called RGD-SVM.
357	An Adaptive Gradient Method for Online AUC Maximization	Yi Ding, Peilin Zhao, Steven C. H. Hoi, Yew-Soon Ong	To overcome the limitation of the existing studies, in this paper, we propose a novel algorithm of Adaptive Online AUC Maximization (AdaOAM), by applying an adaptive gradient method for exploiting the knowledge of historical gradients to perform more informative online learning.
358	Graph-Sparse LDA: A Topic Model with Structured Sparsity	Finale Doshi-Velez, Byron C. Wallace, Ryan Adams	To improve topic interpretability, we introduce Graph-Sparse LDA, a hierarchical topic model that uses knowledge of relationships between words (e.g., as encoded by an ontology).
359	Bayesian Maximum Margin Principal Component Analysis	Changying Du, Shandian Zhe, Fuzhen Zhuang, Yuan Qi, Qing He, Zhongzhi Shi	In this paper, we present a posterior-regularized Bayesian approach to combine Principal Component Analysis (PCA) with the max-margin learning.
360	Modelling Class Noise with Symmetric and Asymmetric Distributions	Jun Du, Zhihua Cai	In classification problem, we assume that the samples around the class boundary are more likely to be incorrectly annotated than others, and propose boundary-conditional class noise (BCN).
361	Optimizing Bag Features for Multiple-Instance Retrieval	Zhouyu Fu, Feifei Pan, Cheng Deng, Wei Liu	Optimizing Bag Features for Multiple-Instance Retrieval
362	Learning Sparse Representations from Datasets with Uncertain Group Structures: Model, Algorithm and Applications	Longwen Gao, Shuigeng Zhou	We formulate the UGSR model and propose an efficient algorithm to solve this problem.
363	Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications	Debarghya Ghoshdastidar, Ambedkar Dukkipati	In this paper, we formulate a criterion for partitioning uniform hypergraphs, and show that a relaxation of this problem is related to the multilinear singular value decomposition (SVD) of symmetric tensors.
364	Pathway Graphical Lasso	Maxim Grechkin, Maryam Fazel, Daniela Witten, Su-In Lee	In this paper, we propose the pathway graphical lasso, which learns the structure of a GGM subject to pathway-based constraints.
365	Concurrent PAC RL	Zhaohan Guo, Emma Brunskill	Building on the efficient exploration RL literature, we introduce two new concurrent RL algorithms and bound their sample complexity.
366	Discriminative Feature Grouping	Lei Han, Yu Zhang	In this paper, we propose a Discriminative Feature Grouping (DFG) method to discover the feature groups with enhanced discrimination.
367	Learning Multi-Level Task Groups in Multi-Task Learning	Lei Han, Yu Zhang	In this paper, we propose a Multi-Level Task Grouping (MeTaG) method to learn the multi-level grouping structure instead of only one level among tasks.
368	Localized Centering: Reducing Hubness in Large-Sample Data	Kazuo Hara, Ikumi Suzuki, Masashi Shimbo, Kei Kobayashi, Kenji Fukumizu, Miloš Radovanović	In this paper, we address a different type of hubness that occurs when the number of samples is large.
369	Expressing Arbitrary Reward Functions as Potential-Based Advice	Anna Harutyunyan, Sam Devlin, Peter Vrancx, Ann Nowe	In this work we give a novel way to incorporate an arbitrary reward function with the same guarantee, by implicitly translating it into the specific form of dynamic advice potentials, which are maintained as an auxiliary value function learnt at the same time.
370	Active Learning by Learning	Wei-Ning Hsu, Hsuan-Tien Lin	More specifically, we design a learning algorithm that connects active learning with the well-known multi-armed bandit problem.
371	Kernelized Online Imbalanced Learning with Fixed Budgets	Junjie Hu, Haiqin Yang, Irwin King, Michael R. Lyu, Anthony Man-Cho So	To tackle this problem, we propose a kernelized online imbalanced learning (KOIL) algorithm to directly maximize the area under the ROC curve (AUC).
372	Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions	De-An Huang, Amir-massoud Farahmand, Kris M. Kitani, James Andrew Bagnell	To enable inference in very large state spaces, we introduce an approximate MaxEnt IOC procedure to address the fundamental computational bottleneck stemming from calculating the partition function via dynamic programming.
373	Maximin Separation Probability Clustering	Gao Huang, Jianwen Zhang, Shiji Song, Zheng Chen	This paper proposes a new approach for discriminative clustering.
374	The Dynamic Chinese Restaurant Process via Birth and Death Processes	Rui Huang, Fengyuan Zhu, Pheng-Ann Heng	We develop the Dynamic Chinese Restaurant Process (DCRP) which incorporates time-evolutionary feature in dependent Dirichlet Process mixture models.
375	Self-Paced Curriculum Learning	Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, Alexander G. Hauptmann	In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self-paced curriculum leaning (SPCL).
376	Outlier-Robust Convex Segmentation	Itamar Katz, Koby Crammer	We describe two algorithms for solving this problem, one exact and one a top-down novel approach, and we derive a consistency results for the case of two segments and no outliers.
377	Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits	Nathaniel Korda, Prashanth L.A., Remi Munos	In the case when strong convexity in the regression problem is guaranteed, we provide bounds on the error both in expectation and high probability (the latter is often needed to provide theoretical guarantees for higher level algorithms), despite the drifting least squares solution.
378	Spectral Learning of Predictive State Representations with Insufficient Statistics	Alex Kulesza, Nan Jiang, Satinder Singh	In this paper we approach the problem both theoretically and empirically.
379	A Generalized Reduced Linear Program for Markov Decision Processes	Chandrashekar Lakshminarayanan, Shalabh Bhatnagar	In this paper, we generalize the RLP to define a generalized reduced linear program (GRLP) which has a tractable number of constraints that are obtained as positive linear combinations of the original constraints of the ALP.
380	Don’t Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX	Johannes Lederer, Christian Müller	In this study, we introduce TREX, an alternative to Lasso with an inherent calibration to all aspects of the model.
381	On the Equivalence of Linear Discriminant Analysis and Least Squares	Kibok Lee, Junmo Kim	In this paper, we verify the equivalence of LDA and least squares (LS) with a set of dependent variable matrices.
382	Multi-tensor Completion with Common Structures	Chao Li, Qibin Zhao, Junhua Li, Andrzej Cichocki, Lili Guo	In this paper, we propose a novel common structure for multi-data learning.
383	Large-Scale Multi-View Spectral Clustering via Bipartite Graph	Yeqing Li, Feiping Nie, Heng Huang, Junzhou Huang	In this paper, we address the problem of large-scale multi-view spectral clustering.
384	Integrating Features and Similarities: Flexible Models for Heterogeneous Multiview Data	Wenzhao Lian, Piyush Rai, Esther Salazar, Lawrence Carin	We present a probabilistic framework for learning with heterogeneous multiview data where some views are given as ordinal, binary, or real-valued feature matrices, and some views as similarity matrices.
385	Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction	Anqi Liu, Lev Reyzin, Brian D. Ziebart	We propose a shift-pessimistic approach to active learning that assumes the worst-case about the unknown conditional label distribution.
386	Unidimensional Clustering of Discrete Data Using Latent Tree Models	April H. Liu, Leonard K.M. Poon, Nevin L. Zhang	We propose a novel method to relax the assumption.
387	Low-Rank Multi-View Learning in Matrix Completion for Multi-Label Image Classification	Meng Liu, Yong Luo, Dacheng Tao, Chao Xu, Yonggang Wen	Therefore, we present a novel multi-view learning model for MC-based image classification, called low-rank multi-view matrix completion (lrMMC), which first seeks a low-dimensional common representation of all views by utilizing the proposed low-rank multi-view learning (lrMVL) algorithm.
388	Support Consistency of Direct Sparse-Change Learning in Markov Networks	Song Liu, Taiji Suzuki, Masashi Sugiyama	In this paper, we give sufficient conditions for successful change detection with respect to the sample size np, nq, the dimension of data m, and the number of changed edges d.
389	Low-Rank Similarity Metric Learning in High Dimensions	Wei Liu, Cun Mu, Rongrong Ji, Shiqian Ma, John R. Smith, Shih-Fu Chang	In this paper, we propose a novel low-rank metric learning algorithm to yield bilinear similarity functions.
390	Large Margin Metric Learning for Multi-Label Prediction	Weiwei Liu, Ivor W Tsang	To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction.
391	Absent Multiple Kernel Learning	Xinwang Liu, Lei Wang, Jianping Yin, Yong Dou, Jian Zhang	This paper proposes an absent MKL (AMKL) algorithm to address this issue.
392	Eigenvalues Ratio for Kernel Selection of Kernel Methods	Yong Liu, Shizhong Liao	In this paper, we propose a novel measure, called eigenvalues ratio (ER), of the tight bound of generalization error for kernel selection.
393	Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation	Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jaillet	To improve its scalability, this paper presents a low-rank-cum-Markov approximation (LMA) of the GP model that is novel in leveraging the dual computational advantages stemming from complementing a low-rank approximate representation of the full-rank GP based on a support set of inputs with a Markov approximation of the resulting residual process; the latter approximation is guaranteed to be closest in the Kullback-Leibler distance criterion subject to some constraint and is considerably more refined than that of existing sparse GP models utilizing low-rank representations due to its more relaxed conditional independence assumption (especially with larger data).
394	Noise-Robust Semi-Supervised Learning by Large-Scale Sparse Coding	Zhiwu Lu, Xin Gao, Liwei Wang, Ji-Rong Wen, Songfang Huang	This paper presents a large-scale sparse coding algorithm to deal with the challenging problem of noise-robust semi-supervised learning over very large data with only few noisy initial labels.
395	The Hybrid Nested/Hierarchical Dirichlet Process and its Application to Topic Modeling with Word Differentiation	Tengfei Ma, Issei Sato, Hiroshi Nakagawa	Specifically, we introduce a clustering structure for the groups.
396	UT Austin Villa 2014: RoboCup 3D Simulation League Champion via Overlapping Layered Learning	Patrick MacAlpine, Mike Depinet, Peter Stone	Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors.
397	The Queue Method: Handling Delay, Heuristics, Prior Data, and Evaluation in Bandits	Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popović	We present the Stochastic Delayed Bandits (SDB) algorithm as a solution to these four problems, which takes black-box bandit algorithms (including heuristic approaches) as input while achieving good theoretical guarantees.
398	V-MIN: Efficient Reinforcement Learning through Demonstrations and Relaxed Reward Demands	David Martínez, Guillem Alenyà, Carme Torras	We present V-MIN, an algorithm that integrates teacher demonstrations with RL to learn complex tasks faster.
399	The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning	Charles Mathy, Nate Derbinsky, Jose Bento, Jonathan Rosenthal, Jonathan Yedidia	We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning.
400	Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners	Shike Mei, Xiaojin Zhu	We investigate a problem at the intersection of machine learning and security: training-set attacks on machine learners.
401	Learning Relational Sum-Product Networks	Aniruddh Nath, Pedro M. Domingos	In this paper, we introduce Relational Sum-Product Networks (RSPNs), a new tractable first-order probabilistic architecture.
402	Tensor-Variate Restricted Boltzmann Machines	Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh	This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables.
403	Probabilistic Attributed Hashing	Mingdong Ou, Peng Cui, Jun Wang, Fei Wang, Wenwu Zhu	In this paper, we propose a hashing learning framework, Probabilistic Attributed Hashing (PAH), to integrate attributes with low-level features.
404	Obtaining Well Calibrated Probabilities Using Bayesian Binning	Mahdi Pakdaman Naeini, Gregory Cooper, Milos Hauskrecht	In this paper we present a new non-parametric calibration method called Bayesian Binning into Quantiles (BBQ) which addresses key limitations of existing calibration methods.
405	Detecting and Tracking Concept Class Drift and Emergence in Non-Stationary Fast Data Streams	Brandon Shane Parker, Latifur Khan	To address all these issues, we propose an incremental semi-supervised method that provides general concept class label predictions, but it also tracks concept clusters within the feature space using an innovative new online clustering algorithm.
406	Detecting Change Points in the Large-Scale Structure of Evolving Networks	Leto Peel, Aaron Clauset	Here, we formalize for the first time the network change-point detection problem within an online probabilistic learning framework and introduce a method that can reliably solve it.
407	Adaptive Sampling with Optimal Cost for Class-Imbalance Learning	Yuxin Peng	To address the above issues, a novel approach of adaptive sampling with optimal cost is proposed for class-imbalance learning in this paper.
408	Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation	Matteo Pirotta, Simone Parisi, Marcello Restelli	This paper is about learning a continuous approximation of the Pareto frontier in Multi-Objective Markov Decision Problems (MOMDPs).
409	Pareto Ensemble Pruning	Chao Qian, Yang Yu, Zhi-Hua Zhou	In this paper, motivated by the recent theoretical advance of evolutionary optimization, we investigate solving the two goals explicitly in a bi-objective formulation and propose the PEP (Pareto Ensemble Pruning) approach.
410	Leveraging Features and Networks for Probabilistic Tensor Decomposition	Piyush Rai, Yingjian Wang, Lawrence Carin	We present a probabilistic model for tensor decomposition where one or more tensor modes may have side-information about the mode entities in form of their features and/or their adjacency network.
411	Doubly Robust Covariate Shift Correction	Sashank Jakkam Reddi, Barnabas Poczos, Alex Smola	We propose a simple strategy for removing bias while retaining small variance.
412	Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery	Peter Schulam, Fredrick Wigley, Suchi Saria	In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers.
413	SP-SVM: Large Margin Classifier for Data on Multiple Manifolds	Bin Shen, Bao-Di Liu, Qifan Wang, Yi Fang, Jan P. Allebach	To benefit from both the underlying low dimensional manifold structure and the large margin classifier, this paper proposes a novel method called Sparsity Preserving Support Vector Machine(SP-SVM), which explicitly considers the sparse representation of samples while maximizing the margin between different classes.
414	Spectral Label Refinement for Noisy and Missing Text Labels	Yangqiu Song, Chenguang Wang, Ming Zhang, Hailong Sun, Qiang Yang	In this paper, we provide a text label refinement algorithm to adjust the labels for such noisy and missing labeled datasets.
415	Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation	Paul A. Szerlip, Gregory Morse, Justin K. Pugh, Kenneth O. Stanley	Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set.
416	Agnostic System Identification for Monte Carlo Planning	Erik Talvitie	This paper explores the interaction between DAgger and Monte Carlo planning, specifically showing that DAgger may perform poorly when coupled with a sub-optimal planner.
417	Optimizing the CVaR via Sampling	Aviv Tamar, Yonatan Glassner, Shie Mannor	Based on this formula, we propose a novel sampling-based estimator for the gradient of the CVaR, in the spirit of the likelihood-ratio method.
418	High-Confidence Off-Policy Evaluation	Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh	In this paper we propose an off-policy method for computing a lower confidence bound on the expected return of a policy.
419	TODTLER: Two-Order-Deep Transfer Learning	Jan Van Haaren, Andrey Kolobov, Jesse Davis	In this paper, we address this issue by regarding transfer learning as a process that biases learning in a target domain in favor of patterns useful in a source domain.
420	Compress and Control	Joel Veness, Marc G Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins	This paper describes a new information-theoretic policy evaluation technique for reinforcement learning.
421	Improving Multi-Step Prediction of Learned Time Series Models	Arun Venkatraman, Martial Hebert, J.. Andrew Bagnell	We present an approach that reuses training data to make a no-regret learner robust to errors made during multi-step prediction.
422	Gaussian Cardinality Restricted Boltzmann Machines	Cheng Wan, Xiaoming Jin, Guiguang Ding, Dou Shen	To solve this problem, we proposed a generalized model with adaptive sparsity constraint, named Gaussian Cardinality Restricted Boltzmann Machines (GC-RBM).
423	Online Boosting Algorithms for Anytime Transfer and Multitask Learning	Boyu Wang, Joelle Pineau	The goal of our work is to provide sound extensions to existing transfer and multitask learning algorithms such that they can be used in an anytime setting.
424	Convex Batch Mode Active Sampling via α-Relative Pearson Divergence	Hanmo Wang, Liang Du, Peng Zhou, Lei Shi, Yi-Dong Shen	In this paper, we propose a novel approach to selecting the optimal batch of queries by minimizing the α-relative Pearson divergence (RPE) between the labeled and the original datasets.
425	Relational Stacked Denoising Autoencoder for Tag Recommendation	Hao Wang, Xingjian Shi, Dit-Yan Yeung	In this paper, we start with a deep learning model called stacked denoising autoencoder (SDAE) in an attempt to learn more effective content representation.
426	Learning Robust Locality Preserving Projection via p-Order Minimization	Hua Wang, Feiping Nie, Heng Huang	In this paper, motivated by existing studies that improve the robustness of statistical learning models via L1-norm or not-squared L2-norm formulations, we propose a robust LPP (rLPP) formulation to minimize the p-th order of the L2-norm distances, which can better tolerate large outlying data samples because it suppress the introduced biased more than the L1-norm or not squared L2-norm minimizations.
427	Learning to Hash on Structured Data	Qifan Wang, Luo Si, Bin Shen	Hashing techniques have been widely applied for large scale similarity search problems due to the computational and memory efficiency.However, most existing hashing methods assume data examples are independently and identically distributed.But there often exists various additional dependency/structure information between data examplesin many real world applications.
428	Transfer Feature Representation via Multiple Kernel Learning	Wei Wang, Hao Wang, Chen Zhang, Fanjiang Xu	In this paper, we generalize the framework of MKL for cross-domain feature learning and propose a novel Transfer Feature Representation (TFR) algorithm.
429	Optimal Estimation of Multivariate ARMA Models	Martha White, Junfeng Wen, Michael Bowling, Dale Schuurmans	Unfortunately, the lack of globally optimal parameter estimation strategies for these models remains a problem:application studies often adopt the simpler autoregressive model that can be easily estimated by maximizing (a posteriori) likelihood.
430	Improving Approximate Value Iteration with Complex Returns by Bounding	Robert William Wright, Xingye Qiao, Steven Loscalzo, Lei Yu	We propose a bounding method which uses a negatively biased but relatively low variance estimator generated from a complex return to provide a lower bound on the observed value of a traditional one-step return estimator.
431	Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time	Ga Wu, Scott Sanner, Rodrigo F.S.C. Oliveira	In this paper, we show for the first time that it is possible to exactly evaluate BMA over the exponentially-sized powerset of NB feature models in linear-time in the number of features; this yields an algorithm about as expensive to train as a single NB model with all features, but yet provably converges to the globally optimal feature subset in the asymptotic limit of data.
432	Dictionary Learning with Mutually Reinforcing Group-Graph Structures	Hongteng Xu, Licheng Yu, Dixin Luo, Hongyuan Zha, Yi Xu	In this paper, we propose a novel dictionary learning method in the semi-supervised setting by dynamically coupling graph and group structures.
433	Active Manifold Learning via Gershgorin Circle Guided Sample Selection	Hongteng Xu, Hongyuan Zha, Ren-Cang Li, Mark A. Davenport	In this paper, we propose an interpretation of active learning from a pure algebraic view and combine it with semi-supervised manifold learning.
434	Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation	Zenglin Xu, Rong Jin, Bin Shen, Shenghuo Zhu	In this paper, we consider the Nystrom approximation for sparse kernel methods.
435	OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation	Yuto Yamaguchi, Christos Faloutsos, Hiroyuki Kitagawa	In this paper, we focus on the node classification problem on networks, which is one of the most important topics in AI and Web communities.
436	Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model	Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada	Gaussian process (GP) regression is a widely used method for non-linear prediction.The performance of the GP regression depends on whether it can properly capture the covariance structure of target variables, which is represented by kernels between input data.However, when the input is represented as a set of features, e.g. bag-of-words, it is difficult to calculate desirable kernel values because the co-occurrence of different but relevant words cannot be reflected in the kernel calculation.To overcome this problem, we propose a Gaussian process latent variable set model (GP-LVSM), which is a non-linear regression model effective for bag-of-words data.With the GP-LVSM, a latent vector is associated with each word, and each document is represented as a distribution of the latent vectors for words appearing in the document.
437	A Mathematical Programming-Based Approach to Determining Objective Functions from Qualitative and Subjective Comparisons	Takayuki Yoshizumi	The solutions or states of optimization problems or simulations are evaluated by using objective functions.
438	Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds	Hongyang Zhang, Zhouchen Lin, Chao Zhang, Edward Y. Chang	In this paper, with incoherence condition and proposed ambiguity condition we prove that Outlier Pursuit succeeds when the rank of the intrinsic matrix is of O(n log n) and the sparsity of the corruption matrix is of O(n).
439	Multi-Source Domain Adaptation: A Causal View	Kun Zhang, Mingming Gong, Bernhard Schoelkopf	Under appropriate assumptions, the availability of multiple source domains allows a natural way to reconstruct the conditional distribution on the target domain; we propose to model PX\|Y (the process to generate effect X from cause Y ) on the target domain as a linear mixture of those on source domains, and estimate all involved parameters by matching the target-domain feature distribution.
440	Online Bandit Learning for a Special Class of Non-Convex Losses	Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou	In this paper, we investigate the problem of online bandit learning with non-convex losses, and develop an efficient algorithm with formal theoretical guarantees.
441	Online Dictionary Learning on Symmetric Positive Definite Manifolds with Vision Applications	Shengping Zhang, Shiva Kasiviswanathan, Pong C. Yuen, Mehrtash Harandi	We make use of the Stein divergence to recast the problem of online dictionary learning on the manifolds to a problem in Reproducing Kernel Hilbert Spaces, for which, we develop efficient algorithms by taking into account the geometric structure of the SPD manifolds.
442	Constrained NMF-Based Multi-View Clustering on Unmapped Data	Xianchao Zhang, Linlin Zong, Xinyue Liu, Hong Yu	In this paper,we tackle the problem of multi-view clustering for unmappeddata in the framework of NMF based clustering.With the help of inter-view constraints, we definethe disagreement between each pair of views by the factthat the indicator vectors of two instances from two differentviews should be similar if they belong to the samecluster and dissimilar otherwise.
443	Multi-Task Learning and Algorithmic Stability	Yu Zhang	In this paper, we study multi-task algorithms from the perspective of the algorithmic stability.
444	SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering	Han Zhao, Pascal Poupart, Yongfeng Zhang, Martin Lysy	We propose SoF (Soft-cluster matrix Factorization), a probabilistic clustering algorithm which softly assigns each data point into clusters.
445	Self-Paced Learning for Matrix Factorization	Qian Zhao, Deyu Meng, Lu Jiang, Qi Xie, Zongben Xu, Alexander G. Hauptmann	To alleviate this deficiency, in this study we present a new MF learning methodology by gradually including matrix elements into MF training from easy to complex.
446	Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision	Yi Zhen, Piyush Rai, Hongyuan Zha, Lawrence Carin	We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images.
447	A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing	Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen	In this paper we introduce a formal and practical reduction between two of the most widely used machine learning algorithms: from the Elastic Net (and the Lasso as a special case) to the Support Vector Machine.
448	10,000+ Times Accelerated Robust Subset Selection	Feiyun Zhu, Bin Fan, Xinliang Zhu, Ying Wang, Shiming Xiang, Chunhong Pan	To address the above two issues, we propose an accelerated robust subset selection (ARSS) method.
449	Tractable Cost-Optimal Planning over Restricted Polytree Causal Graphs	Meysam Aghighi, Peter Jonsson, Simon Ståhlberg	We prove tractability of cost-optimal planning by providing an algorithm based on a novel notion of variable isomorphism.
450	Some Fixed Parameter Tractability Results for Planning with Non-Acyclic Domain-Transition Graphs	Christer Bäckström	In particular, we consider the case where each strongly connected component (SCC) in a DTG must be a simple cycle, and we show that planning is fpt for this case if the causal graph is a polytree.
451	Robustness in Probabilistic Temporal Planning	Jeb Brooks, Emilia Reed, Alexander Gruver, James C. Boerkoel	We introduce a new metric called robustness that measures the likelihood of success for probabilistic temporalplans.
452	SMT-Based Nonlinear PDDL+ Planning	Daniel Bryce, Sicun Gao, David Musliner, Robert Goldman	We present a new technique that accommodates nonlinear change by encoding problems as nonlinear hybrid systems.
453	Strong Temporal Planning with Uncontrollable Durations: A State-Space Approach	Alessandro Cimatti, Andrea Micheli, Marco Roveri	In this paper, we tackle the problem of temporal planning with uncontrollable action durations.
454	Factored MCTS for Large Scale Stochastic Planning	Hao Cui, Roni Khardon, Alan Fern, Prasad Tadepalli	We show that even with moderate increase in the size of existing challenge problems, the performance of state of the art algorithms deteriorates rapidly, making them ineffective.To address this problem we propose a family of simple but scalable online planning algorithms that combine sampling, as in Monte Carlo tree search, with “aggregation,” where the aggregation approximates a distribution over random variables by the product of their marginals.
455	Transition Constraints for Parallel Planning	Nina Ghanbari Ghooshchi, Majid Namazi, M A Hakim Newton, Abdul Sattar	We present a planner named Transition Constraints for Parallel Planning (TCPP).
456	Measuring Plan Diversity: Pathologies in Existing Approaches and A New Plan Distance Metric	Robert P. Goldman, Ugur Kuter	In this paper we present a plan-plan distance metric based on Kolmogorov(Algorithmic) complexity.
457	Efficient Bounds in Heuristic Search Algorithms for Stochastic Shortest Path Problems	Eric A. Hansen, Ibrahim Abdoulahi	We introduce a simple and efficient test for convergence that applies to SSP problems with positive action costs.
458	A Generalization of Sleep Sets Based on Operator Sequence Redundancy	Robert C. Holte, Yusra Alkhazraji, Martin Wehrle	In this paper, we propose a generalization of sleep sets and prove its correctness.
459	Goal Recognition Design for Non-Optimal Agents	Sarah Keren, Avigdor Gal, Erez Karpas	For two special cases of sub-optimal agents we present methods for calculating the wcd, part of which are based on novel compilations to classical planning problems.
460	Variable-Deletion Backdoors to Planning	Martin Kronegger, Sebastian Ordyniak, Andreas Pfandler	In this work we improve the situation by defining a new type of variable-deletion backdoors based on the extended causal graph of a planning instance.
461	Preference Planning for Markov Decision Processes	Meilun Li, Zhikun She, Andrea Turrini, Lijun Zhang	In this paper, we propose the probabilistic preference planning problem for Markov decision processes, where the preferences are based on an enriched probabilistic LTL-style logic.
462	Information Gathering and Reward Exploitation of Subgoals for POMDPs	Hang Ma, Joelle Pineau	In this paper, we propose Information Gathering and Reward Exploitation of Subgoals (IGRES), a randomized POMDP planning algorithm that leverages information in the state space to automatically generate “macro-actions” to tackle tasks with long planning horizons, while locally exploring the belief space to allow effective information gathering.
463	Planning Over Multi-Agent Epistemic States: A Classical Planning Approach	Christian Muise, Vaishak Belle, Paolo Felli, Sheila McIlraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg	In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents.
464	From Non-Negative to General Operator Cost Partitioning	Florian Pommerening, Malte Helmert, Gabriele Röger, Jendrik Seipp	We show that LP heuristics for operator-counting constraints are cost-partitioned heuristics and that the state equation heuristic computes a cost partitioning over atomic projections.
465	Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes	Pascal Poupart, Aarti Malhotra, Pei Pei, Kee-Eung Kim, Bongseok Goh, Michael Bowling	In this work, we describe a technique based on approximate linear programming to optimize policies in CPOMDPs.
466	Discretization of Temporal Models with Application to Planning with SMT	Jussi Rintanen	In this work we investigate the encoding of time in such constraint-based representations.
467	Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection	Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek	We propose a new POMDP planning method that uses greedy maximization to greatly improve scalability in the number of sensors.
468	Automatic Configuration of Sequential Planning Portfolios	Jendrik Seipp, Silvan Sievers, Malte Helmert, Frank Hutter	Here, we present Cedalion, a conceptually simple approach for this problem that greedily searches for the pair of parameter configuration and runtime which, when appended to the current portfolio, maximizes portfolio improvement per additional runtime spent.
469	Heuristics and Symmetries in Classical Planning	Alexander Shleyfman, Michael Katz, Malte Helmert, Silvan Sievers, Martin Wehrle	We investigate the symmetry properties of existing heuristics and reveal that many of them are invariant under symmetries.
470	Factored Symmetries for Merge-and-Shrink Abstractions	Silvan Sievers, Martin Wehrle, Malte Helmert, Alexander Shleyfman, Michael Katz	We propose the concept of factored symmetries for merge-and-shrink abstractions based on the established concept of symmetry reduction for state-space search.
471	Improving Exploration in UCT Using Local Manifolds	Sriram Srinivasan, Erik Talvitie, Michael Bowling	In this paper, weimprove exploration in UCT by generalizing across similarstates using a given distance metric.
472	Tractability of Planning with Loops	Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell	We create a unified framework for analyzing and synthesizing plans with loops for solving problems with non-deterministic numeric effects and a limited form of partial observability.
473	Real-Time Symbolic Dynamic Programming	Luis Gustavo Rocha Vianna, Leliane N. de Barros, Scott Sanner	In this work, wesimultaneously address both of these problems by introducing real-timeSDP (RTSDP).
474	tBurton: A Divide and Conquer Temporal Planner	David Wang, Brian Williams	We present tBurton, a temporal planner that supports these features, while additionally producing a temporally least-commitment plan.
475	Multi-Objective MDPs with Conditional Lexicographic Reward Preferences	Kyle Hollins Wray, Shlomo Zilberstein, Abdel-Illah Mouaddib	We introduce a rich model called Lexicographic MDP (LMDP) and a corresponding planning algorithm called LVI that generalize previous work by allowing for conditional lexicographic preferences with slack.
476	Resolving Over-Constrained Probabilistic Temporal Problems through Chance Constraint Relaxation	Peng Yu, Cheng Fang, Brian Williams	In this paper, we present a decision aid system that helps human operators diagnose the source of risk and manage uncertainty in temporal problems.
477	An Efficient Forest-Based Tabu Search Algorithm for the Split-delivery Vehicle Routing Problem	Zizhen Zhang, Huang He, Zhixing Luo, Hu Qin, Songshan Guo	Differently, our approach employs the combination of a set of routes and a forest to represent the solution.
478	Crowdsourced Action-Model Acquisition for Planning	Hankz Hankui Zhuo	Creating action models is, however, a difficult task that costs much manual effort. Specifically, we first build a set of soft constraints based on the labels (true or false) given by the crowd or annotators.
479	Loss-Calibrated Monte Carlo Action Selection	Ehsan Abbasnejad, Justin Domke, Scott Sanner	In this paper we remedy this problem by deriving an optimal proposal distribution for a loss-calibrated Monte Carlo importance sampler that bounds the regret of using an estimated optimal action.
480	Solving Uncertain MDPs with Objectives that Are Separable over Instantiations of Model Uncertainty	Yossiri Adulyasak, Pradeep Varakantham, Asrar Ahmed, Patrick Jaillet	Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty.
481	Linear-Time Gibbs Sampling in Piecewise Graphical Models	Hadi Mohasel Afshar, Scott Sanner, Ehsan Abbasnejad	Many real-world Bayesian inference problems such as preference learning or trader valuation modeling in financial markets naturally use piecewise likelihoods.
482	Stable Model Counting and Its Application in Probabilistic Logic Programming	Rehan Abdul Aziz, Geoffrey Chu, Christian Muise, Peter James Stuckey	In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks.
483	Recovering Causal Effects from Selection Bias	Elias Bareinboim, Jin Tian	In this paper, we tackle these instances non-parametrically and in full generality.
484	Egalitarian Collective Decision Making under Qualitative Possibilistic Uncertainty: Principles and Characterization	Nahla Ben Amor, Fatma Essghaier, Helene Fargier	This paper raises the question of collective decisionmaking under possibilistic uncertainty; We study fouregalitarian decision rules and show that in the contextof a possibilistic representation of uncertainty, the useof an egalitarian collective utility function allows toget rid of the Timing Effect.
485	Representing Aggregators in Relational Probabilistic Models	David Buchman, David Poole	We consider the problem of, given a probabilistic model on a set of random variables, how to add a new variable that depends on the other variables, without changing the original distribution.
486	Optimal Cost Almost-Sure Reachability in POMDPs	Krishnendu Chatterjee, Martin Chmelik, Raghav Gupta, Ayush Kanodia	The optimization objective we study asks to minimize the expected total cost till the target set is reached, while ensuring that the target set is reached almost-surely (with probability 1).
487	Value of Information Based on Decision Robustness	Suming Jeremiah Chen, Arthur Choi, Adnan Darwiche	We propose a new criterion for measuring the value of information, which values information that leads to robust decisions (i.e., ones that are unlikely to change due to new information).
488	Submodular Surrogates for Value of Information	Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Andrew Bagnell, Siddhartha Srinivasa, Andreas Krause	In this paper, we introduce DiRECt, an efficient yet near-optimal algorithm for nonmyopically optimizing value of information.
489	Bayesian Networks Specified Using Propositional and Relational Constructs: Combined, Data, and Domain Complexity	Fabio Gagliardi Cozman, Denis Deratani Maua	We examine the inferential complexity of Bayesian networks specified through logical constructs.
490	An Improved Lower Bound for Bayesian Network Structure Learning	Xiannian Fan, Changhe Yuan	This work introduces a new partition method based on information extracted from the potential optimal parent sets (POPS) of the variables.
491	Approximately Optimal Risk-Averse Routing Policies via Adaptive Discretization	Darrell Hoy, Evdokia Nikolova	In this paper, we consider general utility functions and investigate efficient computation of approximately optimal routing policies, where the goal is to maximize the expected utility of arriving at a destination around a given deadline.
492	Better Be Lucky than Good: Exceeding Expectations in MDP Evaluation	Thomas Keller, Florian Geißer	We introduce the MDP-Evaluation Stopping Problem, the optimization problem faced by participants of the International Probabilistic Planning Competition 2014 that focus on their own performance.
493	Reward Shaping for Model-Based Bayesian Reinforcement Learning	Hyeoneun Kim, Woosang Lim, Kanghoon Lee, Yung-Kyun Noh, Kee-Eung Kim	In this paper, we present potential-based shaping for improving the learning performance in model-based BRL.
494	Tighter Value Function Bounds for Bayesian Reinforcement Learning	Kanghoon Lee, Kee-Eung Kim	In this paper, we propose a novel approach to compute tighter value function bounds of the Bayes-optimal value function, which is crucial for improving the performance of many model-based BRL algorithms.
495	Knowledge-Based Probabilistic Logic Learning	Phillip Odom, Tushar Khot, Reid Porter, Sriraam Natarajan	Advice giving has been long explored in artificial intelligence to build robust learning algorithms.
496	On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions	Aaditya Ramdas, Sashank Jakkam Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman	We identify different sources of misconception that give rise to the above belief.
497	Representation Discovery for MDPs Using Bisimulation Metrics	Sherry Shanshan Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup	We propose an algorithm to generate an iteratively improving sequence of state space partitions.
498	Lifting Model Sampling for General Game Playing to Incomplete-Information Models	Michael Schofield, Michael Thielscher	We present and analyse a method for escalating reasoning from complete information models to incomplete information models and show how this enables a general game player to correctly value information in incomplete information games.
499	On Interruptible Pure Exploration in Multi-Armed Bandits	Alexander Shleyfman, Antonín Komenda, Carmel Domshlak	We introduce Discriminative Bucketing (DB), a novel family of strategies for pure exploration in MABs, which allows for adapting recent advances in non-interruptible strategies to the interruptible setting, while guaranteeing exponential-rate performance improvement over time.
500	Lifted Probabilistic Inference for Asymmetric Graphical Models	Guy Van den Broeck, Mathias Niepert	We present a framework for probabilistic sampling-based inference that only uses the induced approximate symmetries to propose steps in a Metropolis-Hastings style Markov chain.
501	Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs	Deepak Venugopal, Somdeb Sarkhel, Vibhav Gogate	Deriving from the vast amount of literature on CSPs and graphical models, we propose an exact junction-tree based algorithm for computing the number of solutions of the dynamic CSP, analyze its properties, and show how it can be used to improve the computational complexity of Gibbs sampling and MaxWalksat.
502	Hierarchical Monte-Carlo Planning	Ngo Anh Vien, Marc Toussaint	We propose novel, scalable MCTS methods which integrate atask hierarchy into the MCTS framework, specifically lead-ing to hierarchical versions of both, UCT and POMCP.
503	Chance-Constrained Scheduling via Conflict-Directed Risk Allocation	Andrew J. Wang, Brian C. Williams	The key insight, compared to previous work in probabilistic scheduling, is to decouple the reasoning about temporal and risk constraints.
504	Learning to Reject Sequential Importance Steps for Continuous-Time Bayesian Networks	Jeremy C. Weiss, Sriraam Natarajan, C. David Page	We introduce ReBaSIS, a method that better approximates the target distribution by sampling variable by variable from existing importance samplers and accepting or rejecting each proposed assignment in the sequence: a choice made based on anticipating upcoming evidence.
505	Nonparametric Scoring Rules	Erik Peter Zawadzki, Sebastien Lahaie	In this paper, we provide such a scoring rule based on a nonparametric approach of eliciting a set of samples from the agent and efficiently evaluating the score using kernel methods.
506	On Fairness in Decision-Making under Uncertainty: Definitions, Computation, and Comparison	Chongjie Zhang, Julie A. Shah	To address this issue, this paper introduces egalitarian solution criteria for sequential decision-making under uncertainty, which are based on the maximin principle.
507	An Exact Algorithm for Solving Most Relevant Explanation in Bayesian Networks	Xiaoyuan Zhu, Changhe Yuan	This paper fills the void and introduces a breadth-first branch-and-bound MRE algorithm based on a novel upper bound on GBF.
508	Proximal Operators for Multi-Agent Path Planning	Jose Bento, Nate Derbinsky, Charles Mathy, Jonathan S. Yedidia	In this paper we resolve these limitations.
509	This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction	Morteza Lahijanian, Shaull Almagor, Dror Fried, Lydia E. Kavraki, Moshe Y. Vardi	To relax this limitation, we introduce a method for quantifying the satisfaction of co-safe linear temporal logic specifications, and propose a planner that uses this method to synthesize robot trajectories with the optimal satisfaction value.
510	Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation	Mathew Monfort, Anqi Liu, Brian Ziebart	We address these two challenges, high dimensionality and uncertainty, by employing predictive inverse optimal control methods to estimate a probabilistic model of human motion trajectories.
511	Spatio-Spectral Exploration Combining In Situ and Remote Measurements	David Ray Thompson, David Wettergreen, Greydon Foil, Michael Furlong, Anatha Ravi Kiran	We introduce a new objective function based on spectral unmixing that seeks pure spectral signatures to accurately model diluted remote signals.
512	Robot Learning Manipulation Action Plans by “Watching” Unconstrained Videos from the World Wide Web	Yezhou Yang, Yi Li, Cornelia Fermuller, Yiannis Aloimonos	This paper presents a system that learns manipulation action plans by processing unconstrained videos from the World Wide Web.
513	Efficient Extraction of QBF (Counter)models from Long-Distance Resolution Proofs	Valeriy Balabanov, Jie-Hong Roland Jiang, Mikolas Janota, Magdalena Widl	This paper settles this open problem affirmatively by constructing a linear-time extraction procedure.
514	SAT Modulo Monotonic Theories	Sam Bayless, Noah Bayless, Holger H. Hoos, Alan J. Hu	In this paper, we define the concept of a Boolean monotonic theory and show how to easily build efficient SMT solvers, including effective theory propagation and clause learning, for such theories.
515	On Computing Maximal Subsets of Clauses that Must Be Satisfiable with Possibly Mutually-Contradictory Assumptive Contexts	Philippe Besnard, Eric Grégoire, Jean-Marie JM Lagniez	The method applies for subsets that are maximal with respect to inclusion or cardinality.
516	Strong Bounds Consistencies and Their Application to Linear Constraints	Christian Bessiere, Anastasia Paparrizou, Kostas Stergiou	Hence, we propose two polynomial-time techniques to enforce approximations of these two consistencies on linear constraints.
517	SMT-Based Validation of Timed Failure Propagation Graphs	Marco Bozzano, Alessandro Cimatti, Marco Gario, Andrea Micheli	In this work we address this problem by leveraging efficient Satisfiability Modulo Theories (SMT) engines to perform exhaustive reasoning on TFPGs.
518	Binarisation via Dualisation for Valued Constraints	David A. Cohen, Martin C. Cooper, Peter G. Jeavons, Stanislav Zivny	Using this standard approach any fixed collection of constraints, of arbitrary arity, can be converted to an equivalent set of constraints of arity at most two.
519	SAT-Based Strategy Extraction in Reachability Games	Niklas Een, Alexander Legg, Nina Narodytska, Leonid Ryzhyk	We present the first strategy extraction algorithm for abstract game tree-based game solvers.
520	The Extendable-Triple Property: A New CSP Tractable Class beyond BTP	Philippe Jégou, Cyril Terrioux	In this paper, we propose a new class called ETP for Extendable-Triple Property, which generalizes BTP, by including it.
521	Online Detection of Abnormal Events Using Incremental Coding Length	Jayanta Kumar Dutta, Bonny Banerjee	We present an unsupervised approach for abnormal event detection in videos.
522	A Bayesian Approach to Perceptual 3D Object-Part Decomposition Using Skeleton-Based Representations	Tarek El-Gaaly, Vicky Froyen, Ahmed Elgammal, Jacob Feldman, Manish Singh	We present a probabilistic approach to shape decomposition that creates a skeleton-based shape representation of a 3D object while simultaneously decomposing it into constituent parts.
523	Exploring Semantic Inter-Class Relationships (SIR) for Zero-Shot Action Recognition	Chuang Gan, Ming Lin, Yi Yang, Yueting Zhuang, Alexander G.Hauptmann	To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning.
524	Building Effective Representations for Sketch Recognition	Jun Guo, Changhu Wang, Hongyang Chao	In this work, we study how to build effective representations for sketch recognition.
525	Learning Predictable and Discriminative Attributes for Visual Recognition	Yuchen Guo, Guiguang Ding, Xiaoming Jin, Jianmin Wang	In this paper, we propose a novel method for learning predictable and discriminative attributes.
526	A Local Sparse Model for Matching Problem	Bo Jiang, Jin Tang, Chris Ding, Bin Luo	A Local Sparse Model for Matching Problem
527	Compute Less to Get More: Using ORC to Improve Sparse Filtering	Johannes Lederer, Sergio Guadarrama	In this paper, we connect the performance of Sparse Filtering with spectral properties of the corresponding feature matrices.
528	Sparse Deep Stacking Network for Image Classification	Jun Li, Heyou Chang, Jian Yang	Therefore, we propose a sparse SNNM module by adding the mixed-norm regularization (l1/l2 norm).
529	Surpassing Human-Level Face Verification Performance on LFW with GaussianFace	Chaochao Lu, Xiaoou Tang	To enhance discriminative power, we introduced a more efficient equivalent form of Kernel Fisher Discriminant Analysis to DGPLVM.To speed up the process of inference and prediction, we exploited the low rank approximation method.
530	Automatic Topic Discovery for Multi-Object Tracking	Wenhan Luo, Björn Stenger, Xiaowei Zhao, Tae-Kyun Kim	This paper proposes a new approach to multi-object tracking by semantic topic discovery.
531	Robust Subspace Clustering via Thresholding Ridge Regression	Xi Peng, Zhang Yi, Huajin Tang	In this paper, we present a new method of robust subspace clustering by eliminating the effect of the errors from the projection space (representation) rather than from the input space.
532	Multi-View Point Registration via Alternating Optimization	Junchi Yan, Jun Wang, Hongyuan Zha, Xiaokang Yang, Stephen M. Chu	We propose a novel multi-view registration method, where the optimal registration is achieved via an efficient and effective alternating concave minimization process.
533	Complex Event Detection via Event Oriented Dictionary Learning	Yan Yan, Yi Yang, Haoquan Shen, Deyu Meng, Gaowen Liu, Alex Hauptmann, Nicu Sebe	In this paper, we propose two novel strategies to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions.
534	Deep Representation Learning with Target Coding	Shuo Yang, Ping Luo, Chen Change Loy, Kenneth W. Shum, Xiaoou Tang	In this paper, we show that there exists intrinsic relationship between target coding and feature representation learning in deep networks.
535	Learning to Describe Video with Weak Supervision by Exploiting Negative Sentential Information	Haonan Yu, Jeffrey Mark Siskind	In this paper, we learn to describe video by discriminatively training positive sentential labels against negative ones in a weakly supervised fashion: the meaning representations (i.e., HMMs) of individual words in these labels are learned from whole sentences without any correspondence annotation of what those words denote in the video.
536	Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking	Liming Zhao, Xi Li, Jun Xiao, Fei Wu, Yueting Zhuang	To address this issue, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning.
537	Learning Face Hallucination in the Wild	Erjin Zhou, Haoqiang Fan, Zhimin Cao, Yuning Jiang, Qi Yin	In this paper, we present a new method of face hallucination, which can consistently improve the resolution of face images even with large appearance variations.
538	A Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling	Chao Zhu, Yuxin Peng	In this paper, we consider pedestrian detection in different occlusion levels as different but related problems, and propose a multi-task model to jointly consider their relatedness and differences.
539	Activity Planning for a Lunar Orbital Mission	John L. Bresina	We present the approach taken to reduce the complexity of the activity planning task in order to effectively perform it within the time pressures imposed by the mission requirements.
540	Robust System for Identifying Procurement Fraud	Amit Dhurandhar, Rajesh Ravi, Bruce Graves, Gopikrishnan Maniachari, Markus Ettl	In this paper, we describe a robust tool to identify procurement related fraud/risk, though the general design and the analytical components could be adapted to detecting fraud in other domains.
541	Position Assignment on an Enterprise Level Using Combinatorial Optimization	Leonard Kinnaird-Heether, Chris Dorman	This paper will describe the creation of this tool and how we have applied it, focusing on the need for developing such a tool, and how the continued development of this tool will benefit its users and the company.
542	Graph Analysis for Detecting Fraud,Waste, and Abuse in Healthcare Data	Juan Liu, Eric Bier, Aaron Wilson, Tomo Honda, Sricharan Kumar, Leilani Gilpin, John Guerra-Gomez, Daniel Davies	In this paper, we describe a system to detect suspicious activities in large healthcare claims datasets.
543	Planned Protest Modeling in News and Social Media	Sathappan Muthiah, Bert Huang, Jaime Arredondo, David Mares, Lise Getoor, Graham Katz, Naren Ramakrishnan	We develop such a system in this paper, using a combination of key phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future tense mentions.
544	Process Diagnosis System (PDS) – A 30 Year History	Edward D. Thompson, Ethan Frolich, James C. Bellows, Benjamin E. Bassford, Edward J. Skiko, Mark S. Fox	Process Diagnosis System (PDS) – A 30 Year History
545	Named Entity Recognition in Travel-Related Search Queries	Brooke Cowan, Sven Zethelius, Brittany Luk, Teodora Baras, Prachi Ukarde, Daodao Zhang	This paper describes an efficient machine learning-based solution for the high-quality extraction of semantic entities from query inputs in a restricted-domain information retrieval setting.
546	Automated Problem List Generation from Electronic Medical Records in IBM Watson	Murthy Devarakonda, Ching-Huei Tsou	We developed a machine learning technique in IBM Watson to automatically generate a patient’s medical problem list.
547	Using Qualitative Spatial Logic for Validating Crowd-Sourced Geospatial Data	Heshan Du, Hai Nguyen, Natasha Alechina, Brian Logan, Michael Jackson, John Goodwin	We describe a tool, MatchMaps, that generates sameAs and partOf matches between spatial objects (such as shops, shopping centres, etc.) in crowd-sourced and authoritative geospatial datasets.
548	Day-Ahead Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models	David John Gagne II, Amy McGovern, Jerald Brotzge, Michael Coniglio, James Correia Jr., Ming Xue	We have developed an approach to forecasting hail that identifies potential hail storms in storm-scale numerical weather prediction models and matches them with observed hailstorms.
549	Capturing Human Route Preferences From Track Information: New Results	Johnathan Gohde, Mark Boddy, Hazel Shackleton, Steve Johnston	In this paper, we report on an extension to G2I2, called GUIDE, which adds significant new capabilities.
550	Design and Experiment of a Collaborative Planning Service for NetCentric International Brigade Command	Christophe Guettier, Willy Lamal, Israël Mayk, Jacques Yelloz	This paper focuses on two areas of interest: decision support based on automated planning and Service Oriented Architecture (SOA) for rapid service development.
551	Aggregating User Input in Ecology Citizen Science Projects	Greg Hines, Alexandra Swanson, Margaret Kosmala, Chris Lintott	In this paper, we present a new aggregation algorithm which achieves an accuracy of 98.6\%, better than many human experts.
552	Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources	Sasin Janpuangtong, Dylan A. Shell	This paper describes an end-to-end learning framework that allows a novice to create a model from data easily by helping structure the model building process and capturing extended aspects of domain knowledge.
553	HACKAR: Helpful Advice for Code Knowledge and Attack Resilience	Ugur Kuter, Mark Burstein, J. Benton, Daniel Bryce, Jordan Thayer, Steve McCoy	This paper describes a novel combination of Java program analysis and automated learning and planning architecture to the domain of Java vulnerability analysis.
554	A Robust and Extensible Tool for Data Integration Using Data Type Models	Andres Quiroz, Eric Huang, Luca Ceriani	We propose HiperFuse, which significantly automates the data integration process by providing a declarative interface, robust type inference, extensible domain-specific data models, and a data integration planner which optimizes for plan completion time.
555	Maestoso: An Intelligent Educational Sketching Tool for Learning Music Theory	Paul Taele, Laura Barreto, Tracy Hammond	In this paper, we describe Maestoso, an educational tool for novice learners to learn music theory through sketching practice of quizzed music structures.
556	Preventing HIV Spread in Homeless Populations Using PSINET	Amulya Yadav, Leandro Soriano Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, Heather Carmichael	Thus, we developed PSINET, a decision support system to aid the agencies in this task.
557	SKILL: A System for Skill Identification and Normalization	Meng Zhao, Faizan Javed, Ferosh Jacob, Matt McNair	In this paper we propose an automated approach for skill entity recognition and optimal normalization.
558	Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!	Peter Clark	Here we propose this task as a challenge problem for the community, and are providing supporting datasets.
559	Time-Varying Clusters in Large-Scale Flow Cytometry	Jeremy Hyrkas, Daniel Halperin, Bill Howe	We describe the problem, the data, and some preliminary results demonstratingthe difficulty with conventional methods.
560	The Winograd Schema Challenge: Evaluating Progress in Commonsense Reasoning	Leora Morgenstern, Charles Ortiz	This paper describes the Winograd Schema Challenge (WSC), which has been suggested as an alternative to the Turing Test and as a means of measuring progress in commonsense reasoning.
561	Speech Adaptation in Extended Ambient Intelligence Environments	Bonnie J. Dorr, Lucian Galescu, Ian Perera, Kristy Hollingshead-Seitz, David Atkinson, Micah Clark, William Clancey, Yorick Wilks, Eric Fosler-Lussier	We suggest that the application ofdivergence detection to speech patterns may enable adaptation to a speaker’s increasing or decreasing level of speech impairment over time.
562	Intelligent Agents for Rehabilitation and Care of Disabled and Chronic Patients	Sarit Kraus	We will discuss the challenges of building an agent for the health care domain and present four capabilities that are required for an agent in the health care domain: planning, monitoring, intervention and encouragement.
563	Mechanism Learning with Mechanism Induced Data	Tie-Yan Liu, Wei Chen, Tao Qin	As shown in this paper, there are many interesting research topics along this direction, many of which are still open problems, waiting for researchers in our community to deeply investigate.
564	Emerging Architectures for Global System Science	Michela Milano, Pascal Van Hentenryck	This paper addresses emergent architectures enabling controlling, predicting and reaoning on these systems.
565	Blended Planning and Acting: Preliminary Approach, Research Challenges	Dana S. Nau, Malik Ghallab, Paolo Traverso	We describe some first steps toward developing such a formalization, and invite readers to carry out research along this line.
566	Impact of Modeling Languages on the Theory and Practice in Planning Research	Jussi Rintanen	We propose revisions to the research agenda in Automated Planning.
567	Steering Evolution Strategically: Computational Game Theory and Opponent Exploitation for Treatment Planning, Drug Design, and Synthetic Biology	Tuomas Sandholm	This has proven to be a key difficulty in developing therapies, since the organisms evolve resistance.I propose the wild idea of steering evolution strategically — using computational game theory for (typically incomplete-information) multistage games and opponent exploitation techniques.
568	Towards a Programmer’s Apprentice (Again)	Howard Elliot Shrobe, Boris Katz, Randall Davis	Towards a Programmer’s Apprentice (Again)
569	Conducting Neuroscience to Guide the Development of AI	Jeffrey Mark Siskind	Techniques like these can be used to study how the human brain grounds language in visual perception and may motivate development of novel approaches in AI.
570	Challenges in Resource and Cost Allocation	Toby Walsh	Many models and mechanisms in resource and cost allocation have been developed that are simple and abstract.
571	Explaining Watson: Polymath Style	Wlodek W. Zadrozny, Valeria de Paiva, Lawrence S. Moss	We present several arguments as we discuss the system.
572	Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education	Xiaojin Zhu	I draw the reader’s attention to machine teaching, the problem of finding an optimal training set given a machine learning algorithm and a target model.
573	Building Strong Semi-Autonomous Systems	Shlomo Zilberstein	We examine the broad rationale for semi-autonomy and define basic properties of such systems.
574	Achieving Intelligence Using Prototypes, Composition, and Analogy	Vinay K. Chaudhri	In this paper, I summarize the results of a decade-plus of research and development driven by the vision that human knowledge can be grounded in a small number of prototypical components that can be extended through composition and analogy.
575	Towards User-Adaptive Information Visualization	Cristina Conati, Giuseppe Carenini, Dereck Toker, Sébastien Lallé	This paper summarizes an ongoing multi-year project aiming to uncover knowledge and techniques for devising intelligent environments for user-adaptive visualizations.
576	Languages for Learning and Mining	Luc De Raedt	This note provides a gentle introduction to three types of languages that support machine learning and data mining: inductive query languages, which extend database query languages with primitives for mining and learning, modelling languages, which allow to declaratively specify and solve mining and learning problems, and programming languages, that support the learning of functions and subroutines.
577	Compile!	Pierre Marquis	In this paper, the focus is laid on three KC topics which gave rise to many works: the development of knowledge compilation techniques for the clausal entailment problem in propositional logic, the concept of compilability and the notion of knowledge compilation map.
578	On the Diagnosis of Cyber-Physical Production Systems	Oliver Niggemann, Volker Lohweg	Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly.
579	Abstraction for Solving Large Incomplete-Information Games	Tuomas Sandholm	In this paper, I will review key developments in the field.
580	Semantic Representation	Lenhart K. Schubert	This paper provides a brief “opinionated survey” of broad-coverage semantic representation (SR).
581	A Succinct Conceptualization of the Foundations for a Network Organization Paradigm	Saad Alqithami	The paper concisely proposes a distinguishing paradigm to study a very large, collective group of agents that is called Network Organization.
582	A Goal-Based Model of Personality for Planning-Based Narrative Generation	Julio Cesar Bahamon, Camille Barot, R. Michael Young	We present an approach to incorporate interesting and compelling characters in planning-based narrative generation.
583	Leveraging Common Structure to Improve Prediction across Related Datasets	Matt Barnes, Nick Gisolfi, Madalina Fiterau, Artur Dubrawski	As standard outlier detection and robust classification usually fall short of determining groups of spurious samples, we propose a procedure which identifies the common structure across datasets by minimizing a multi-dataset divergence metric, increasing accuracy for new datasets.
584	Stochastic Blockmodeling for Online Advertising	Li Chen, Matthew Patton	In this paper, we introduce a stochastic blockmodeling for the website relations induced by the event of online user visitation.
585	Query Abduction for ELH Ontologies	Mahsa Chitsaz, Zhe Wang, Kewen Wang	In this paper, we develop a sound and complete algorithm of query abduction for general conjunctive queries in ELH ontologies.
586	A Multi-Pass Sieve for Name Normalization	Jennifer D'Souza	We propose a simple multi-pass sieve framework that applies tiers of deterministic normalization modules one at a time from highest to lowest precision for the task of normalizing names.
587	A Sequence Labeling Approach to Deriving Word Variants	Jennifer D'Souza	This paper describes a learning-based approach for automatic derivation of word variant forms bythe suffixation process.
588	Predicting the Quality of User Experiences to Improve Productivity and Wellness	Priya Lekha Donti, Jacob Rosenbloom, Alex Gruver, James Jr C. Boerkoel	In order to test these hypotheses, we introduce the Productivity and Wellness Pal (PaWPal), a smartphone-based application that seeks to make users aware of their efficacy at various tasks as well as which courses of action are likely to lead to immersive experiences.
589	Modelling Individual Negative Emotion Spreading Process with Mobile Phones	Zhanwei Du, Yongjian Yang, Chuang Ma, Yuan Bai	In this paper, we propose a Graph-Coupled Hidden Markov Sentiment Model for modeling the propagation of infectious negative sentiment locally within a social network.
590	Active Learning for Informative Projection Retrieval	Madalina Fiterau, Artur Dubrawski	We introduce an active learning framework designed to train classification models which use informative projections.
591	Placing Influencing Agents in a Flock	Katie Genter, Peter Stone	In our ongoing work highlighted in this abstract, we are specifically considering the problem of where to initially place influencing agents that we add to such a flock.
592	Characterizing Performance of Consistency Algorithms by Algorithm Configuration of Random CSP Generators	Daniel J. Geschwender, Robert J. Woodward, Berthe Y. Choueiry	In order to understand what problem features lead to better performance of one algorithm over another, we utilize an algorithm configurator to tune the parameters of a random problem generator and maximize the performance difference of two consistency algorithms for enforcing constraint minimality.
593	Finding Meaningful Gaps to Guide Data Acquisition for a Radiation Adjudication System	Nick Gisolfi, Madalina Fiterau, Artur Dubrawski	Intended for use when data acquisition is an iterative process controlled by domain experts, our method exposes insufficiencies of training data and presents them in a user-friendly manner.
594	Dealing with Trouble: A Data-Driven Model of a Repair Type for a Conversational Agent	Sviatlana Höhn	Here I describe a data-driven model for simulation of dialogue sequences where the learner user does not understand the talk of a conversational agent in chat and asks for clarification.
595	On Manipulablity of Random Serial Dictatorship in Sequential Matching with Dynamic Preferences	Hadi Hosseini, Kate Larson, Robin Cohen	We propose a generic framework for evaluating sequential matching mechanisms with dynamic preferences, and show that unlike single-shot settings, the random serial dictatorship mechanism is manipulable.
596	Language Independent Feature Extractor	Young-Seob Jeong, Ho-Jin Choi	We propose a new customizable tool, Language Independent Feature Extractor (LIFE), which models the inherent patterns of any language and extracts relevant features of thelanguage.
597	Coupled Collaborative Filtering for Context-aware Recommendation	Xinxin Jiang, Wei Liu, Longbing Cao, Guodong Long	We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations.
598	Sorted Neighborhood for the Semantic Web	Mayank Kejriwal	Sorted Neighborhood for the Semantic Web
599	Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory	Mackenzie Leake, Liyu Xia, Kamil Rocki, Wayne Imaino	Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory
600	Acronym Disambiguation Using Word Embedding	Chao Li, Lei Ji, Jun Yan	In this paper, we propose two word embedding based models for acronym disambiguation.
601	Learning Word Vectors Efficiently Using Shared Representations and Document Representations	Qun Luo, Weiran Xu	We propose some better word embedding models based on vLBL model and ivLBL model by sharing representations between context and target words and using document representations.
602	Just-in-Time Hierarchical Constraint Decomposition	Valentin Mayer-Eichberger	My research project extends the LCG methodology by using a mix of eager and lazy encodings and a richer set of constraint decompositions.
603	Every Team Deserves a Second Chance: Identifying When Things Go Wrong (Student Abstract Version)	Vaishnavh Nagarajan, Leandro Soriano Marcolino, Milind Tambe	We show that without using any domain knowledge, we can predict the final performance of a team of voting agents, at any step towards solving a complex problem.
604	Active Advice Seeking for Inverse Reinforcement Learning	Phillip Odom, Sriraam Natarajan	We consider the problem of actively soliciting human advice in an inverse reinforcement learning setting where the utilities are learned from demonstrations.
605	Designing Vaccines that Are Robust to Virus Escape	Swetasudha Panda, Yevgeniy Vorobeychik	We propose a three-pronged approach to address this: first, application of local search, using a native antibody sequence as leverage, second, machine learning to predict binding for antibody-virus pairs, and third, a poisson regression to predict escape costs as a function of antibody sequence assignment.
606	“Is It Rectangular?” Using I Spy as an Interactive, Game-Based Approach to Multimodal Robot Learning	Natalie Paige Parde, Michalis Papakostas, Konstantinos Tsiakas, Rodney D. Nielsen	“Is It Rectangular?” Using I Spy as an Interactive, Game-Based Approach to Multimodal Robot Learning
607	A New Computational Intelligence Model for Long-Term Prediction of Solar and Geomagnetic Activity	Mahboobeh Parsapoor, John Brooke, Bertil Svensson	This paper briefly describes how the neural structure of fear conditioning has inspired to develop a computational intelligence model that is referred to as the brain emotional learning-inspired model (BELIM).
608	GEF: A Self-Programming Robot Using Grammatical Evolution	Charles Peabody, Jennifer H. Seitzer	In this work, we present GEF (“Grammatical Evolution for the Finch”), a system that employs grammatical evolution to create a Finch robot controller program in Java.
609	Planning with Numeric Timed Initial Fluents	Chiara Piacentini, Maria Fox, Derek Long	In this paper we present an extension of the planner POPF2 (POPF-TIF) to handle problems with numeric Timed Initial Fluents.
610	Combining Ontology Class Expression Generation with Mathematical Modeling for Ontology Learning	Jedrzej Potoniec, Agnieszka Ławrynowicz	We present an idea of using mathematicall modelling to guide a process of mining a set of patterns in an RDF graph and further exploiting these patterns to build expressive OWL class hierarchies.
611	Improving Cross-Domain Recommendation through Probabilistic Cluster-Level Latent Factor Model	Siting Ren, Sheng Gao, Jianxin Liao, Jun Guo	To capture diversities among different domains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF) model to improve the cross-domain recommendation performance.
612	Representation Discovery for MDPs Using Bisimulation Metrics	Sherry Shanshan Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup	We propose an algorithm to generate an iteratively improving sequence of state space partitions.
613	Graphical Representation of Assumption-Based Argumentation	Claudia Schulz	Since Assumption-Based Argumentation (ABA) was introduced in the nineties,the structure and semantics of an ABA framework have been studied exclusively in logical termswithout any graphical representation.Here, we show how an ABA framework and its complete semantics can be displayed in a graph,clarifying the structure of the ABA framework as well as the resulting complete assumption labellings.Furthermore, we show that such an ABA graph can be used to represent the structureand semantics of a logic program (LP), based on the correspondence between the semantics of a LP and an ABA framework encoding this LP.
614	Actionable Combined High Utility Itemset Mining	Jingyu Shao, Junfu Yin, Wei Liu, Longbing Cao	In this paper, we introduce the concept of combined mining to select combined itemsets that are not only high utility and high frequency, but also involving relations between itemsets.
615	Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?	Samta Shukla, Aditya Telang, Salil Joshi, L. Venkat Subramaniam	In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user.
616	Multimedia Data for the Visually Impaired	Niket Tandon, Shekhar Sharma, Tanima Makkad	We propose a model to automatically identify such videos.
617	Improving Microblog Retrieval from Exterior Corpus by Automatically Constructing Microblogging Corpus	Wenting Tu, David Cheung, Nikos Mamoulis	To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus.
618	Time-Sensitive Opinion Mining for Prediction	Wenting Tu, David Cheung, Nikos Mamoulis	This short paper presents our preliminary work on extracting reference time tagsand integrating them into an opinion mining model, in order to improvethe accuracy of future event prediction.
619	Self-Organized Collective Decision-Making in a 100-Robot Swarm	Gabriele Valentini, Heiko Hamann, Marco Dorigo	We study a self-organized collective decision-making strategy to solve a site-selection problem using a swarm of simple robots.
620	Handling Uncertainty in Answer Set Programming	Yi Wang, Joohyung Lee	We present a probabilistic extension of logic programs under the stable model semantics, inspired by the concept of Markov Logic Networks.
621	Combining Machine Learning and Crowdsourcing for Better Understanding Commodity Reviews	Heting Wu, Hailong Sun, Yili Fang, Kefan Hu, Yongqing Xie, Yangqiu Song, Xudong Liu	In this work, we combine machine learning and crowdsourcing together for better understanding customer reviews.
622	What Is the Longest River in the USA? Semantic Parsing for Aggregation Questions	Kun Xu, Sheng Zhang, Yansong Feng, Songfang Huang, Dongyan Zhao	What Is the Longest River in the USA? Semantic Parsing for Aggregation Questions
623	Accelerating SAT Solving by Common Subclause Elimination	Yaowei Yan, Chris E. Gutierrez, Jeriah Jn-Charles, Forrest Sheng Bao, Yuanlin Zhang	In this paper, we present novel algorithms for fast SAT solving by employing two common subclause elimination (CSE) approaches.
624	Global Policy Construction in Modular Reinforcement Learning	Ruohan Zhang, Zhao Song, Dana H. Ballard	We propose a modular reinforcement learning algorithm which decomposes a Markov decision process into independent modules.
625	Touchless Telerobotic Surgery — Is It Possible at All?	Tian Zhou, Maria Eugenia Cabrera, Juan Pablo Wachs	This paper presents a comprehensive evaluation among touchless, vision-based hand tracking interfaces (Kinect and Leap Motion) and the feasibility of their adoption into the surgical theater compared to traditional interfaces.
626	Modeling Eye Movements when Reading Microblogs	Maria Barrett, Anders Soegaard	This PhD project aims at a quantitative description of reading patterns from eye movements when reading tweets and the development of an eye movement relevance model.
627	Exploiting the Structure of Distributed Constraint Optimization Problems	Ferdinando Fioretto	In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost function.
628	Realistic Assumptions for Attacks on Elections	Zack Fitzsimmons	I propose further study into modeling realistic election attacks and the advancement of the current state of empirical analysis of their hardness by using more advanced statistical techniques.
629	Social Hierarchical Learning	Bradley Hayes	I approach this problem through advancing the state of the art in building hierarchical task representations, multi-agent task-level planning, and learning assistive behaviors from demonstration.
630	Multivariate Conditional Anomaly Detection and Its Clinical Application	Charmgil Hong, Milos Hauskrecht	This paper overviews the background, goals, past achievements and future directions of our research that aims to build a multivariate conditional anomaly detection framework for the clinical application.
631	Probabilistic Planning with Risk-Sensitive Criterion	Ping Hou	In our recent paper (Hou, Yeoh, and Varakantham 2014), we formally defined Risk-Sensitive MDPs (RS-MDPs) and introduced new algorithms for RS-MDPs with non-negative costs.
632	Entity Resolution in a Big Data Framework	Mayank Kejriwal	The dissertation aims to build such a system and evaluate it on real-world datasets published already as Linked Open Data.
633	Non-Classical Planning for Robotic Applications	Scott Kiesel	For my dissertation I am focusing on non-classical planning for robotic applications.
634	Transfer Learning-Based Co-Run Scheduling for Heterogeneous Datacenters	Wei Kuang, Laura E. Brown, Zhenlin Wang	We extend the models to consider cross-architecture sensitivity (across different machines).
635	HVAC-Aware Occupancy Scheduling (Extended Abstract)	Boon-Ping Lim	My research focuses on developing innovative ways to control Heating, Ventilation, and Air Conditioning (HVAC) and schedule occupancy flows in smart buildings to reduce our ecological footprint (and energy bills).
636	Scalable Agent Modeling for Large Multiagent Systems	Carrie Rebhuhn	We develop an approach where an agent can learn using an abstract model identification orstereotype rather than an explicit and unique model for each agent.
637	Explaining Answer Set Programming in Argumentative Terms	Claudia Schulz	Argumentation Theory and Answer Set Programming (ASP) are two prominent theories in the field of knowledge representation and non-monotonic reasoning,where Argumentation Theory stands for a variety of approaches following similar ideas.The main difference between Argumentation Theory and ASP is that the former focusses on representing knowledge and reasoning about it in a way that resembles human reasoning, neglecting the efficiency of the reasoning procedure,whereas the latter is concerned with the efficient computation of solutions to a reasoning problem, resulting in a less human-understandable process.
638	Optimal Multi-Agent Pathfinding Algorithms	Guni Sharon	The goal of my research is providing new methods to solve MAPF optimally and provide theoretical understandings that will help choose the best solver given a problem instance.
639	Multi-Agent Team Formation: Solving Complex Problems by Aggregating Opinions	Leandro Soriano Marcolino	In my thesis, I present two different novel models to aid in the team formation process.
640	Scaling-Up Inference in Markov Logic	Deepak Venugopal	The aim of this thesis is to advance the state-of-the-art in MLN inference, enabling it to solve much harder and more complex tasks than is possible today.
641	Risk-Aware Scheduling throughout Planning and Execution	Andrew J. Wang	Risk-Aware Scheduling throughout Planning and Execution
642	A Planning-Based Assistance System for Setting Up a Home Theater	Pascal Bercher, Felix Richter, Thilo Hörnle, Thomas Geier, Daniel Höller, Gregor Behnke, Florian Nothdurft, Frank Honold, Wolfgang Minker, Michael Weber, Susanne Biundo	We present a system that assists a human user in setting up a complex home theater consisting of several HiFi devices.
643	On Correcting Misspelled Queries in Email Search	Abhijit Bhole, Raghavendra Udupa	We propose SpEQ, a Machine Learning based approach that generates cor- rections for misspelled queries directly from the user’s own mail data.
644	Towards Cognitive Automation of Data Science	Alain Biem, Maria Butrico, Mark Feblowitz, Tim Klinger, Yuri Malitsky, Kenney Ng, Adam Perer, Chandra Reddy, Anton Riabov, Horst Samulowitz, Daby Sow, Gerald Tesauro, Deepak Turaga	This work presents a step towards this goal.
645	Tartanian7: A Champion Two-Player No-Limit Texas Hold’em Poker-Playing Program	Noam Brown, Sam Ganzfried, Tuomas Sandholm	We introduce a distributed version of the most commonly used equilibrium-finding algorithm, counterfactual regret minimization (CFR), which enables CFR to scale to dramatically larger abstractions and numbers of cores.
646	CrowdMR: Integrating Crowdsourcing with MapReduce for AI-Hard Problems	Jun Chen, Chaokun Wang, Yiyuan Bai	In this paper, we integrated crowdsourcing with MapReduce to provide a scalable innovative human-machine solution to AI-hard problems, which is called CrowdMR.
647	VecLP: A Realtime Video Recommendation System for Live TV Programs	Sheng Gao, Dai Zhang, Honggang Zhang, Chao Huang, Yongsheng Zhang, Jianxin Liao, Jun Guo	We propose VecLP, a novel Internet Video recommendation system working for Live TV Programs in this paper.
648	Multi-Agent Dynamic Coupling for Cooperative Vehicles Modeling	Maxime Guériau, Romain Billot, Nour-Eddin El Faouzi, Salima Hassas, Frédéric Armetta	We present our multi-agent model, tested through simulations using real traffic data and integrated into our extension of the Multi-model Open-source Vehicular-traffic SIMulator (MovSim).
649	Cognitive Master Teacher	Raghu Krishnapuram, Luis A Lastras, Satya Nitta	Cognitive Master Teacher
650	Visualizing Inference	Henry Lieberman, Joe Henke	We present a typical scenario of using Alar to debug a knowledge base.
651	Bottom-Up Demand Response by Following Local Energy Generation Voluntarily	Tobias Linnenberg, Alexander Fay, Michael Kaisers	We present an open-source low-budget hardware and software prototype of a smart plug, and the principles behind its capability to align power demand with a ref- erence signal, e.g. from local renewable energy genera- tion.
652	Visualization Techniques for Topic Model Checking	Jaimie Murdock, Colin Allen	We present the Topic Explorer, which advances the state-of-the-art in topic model visualization for document-document and topic-document relations.
653	Salient Object Detection via Objectness Proposals	Tam Van Nguyen	This paper presents a real-time system that detects salient object by integrating objectness, foreground and compactness measures.
654	Gene Selection in Microarray Datasets Using Progressively Refined PSO Scheme	Yamuna Prasad, K. K. Biswas	In this paper we propose a wrapper based PSO method for gene selection in microarray datasets, where we gradually refine the feature (gene) space from a very coarse level to a fine grained one, by reducing the gene set at each step of the algorithm.
655	World WordNet Database Structure: An Efficient Schema for Storing Information of WordNets of the World	Hanumant Harichandra Redkar, Sudha Baban Bhingardive, Diptesh Kanojia, Pushpak Bhattacharyya	In this paper, we present the World WordNet Database Structure which can be used to efficiently store the WordNet information of all languages of the World.
656	The Network Data Repository with Interactive Graph Analytics and Visualization	Ryan Rossi, Nesreen Ahmed	The Network Data Repository with Interactive Graph Analytics and Visualization
657	DeepTutor: An Effective, Online Intelligent Tutoring System That Promotes Deep Learning	Vasile Rus, Nobal Niraula, Rajendra Banjade	We present in this paper an innovative solution to the challenge of building effective educational technologies that offer tailored instruction to each individual learner.
658	Inferring Latent User Properties from Texts Published in Social Media	Svitlana Volkova, Yoram Bachrach, Michael Armstrong, Vijay Sharma	We demonstrate an approach to predict latent personal attributes including user demographics, online personality, emotions and sentiments from texts published on Twitter.
659	Circumventing Robots’ Failures by Embracing Their Faults: A Practical Approach to Planning for Autonomous Construction	Stefan Witwicki, Francesco Mondada	We describe how embracing these faults leads to better representations and smarter planning, allowing robots with limited precision to avoid catastrophic failures and succeed in intricate constructions.
660	Crowd Motion Monitoring with Thermodynamics-Inspired Feature	Xinfeng Zhang, Su Yang, Yuan Yan Tang, Weishan Zhang	Inspired by that, we introduce Boltzmann Entropy to measure crowd motion in optical flow field so as to detect abnormal collective behaviors.
661	Cerebella: Automatic Generation of Nonverbal Behavior for Virtual Humans	Margot Lhommet, Yuyu Xu, Stacy Marsella	Cerebella: Automatic Generation of Nonverbal Behavior for Virtual Humans
662	Scheherazade: Crowd-Powered Interactive Narrative Generation	Boyang Li, Mark Riedl	Interactive narrative is a form of storytelling in which users affect a dramatic storyline through actions by assuming the role of characters in a virtual world.This extended abstract outlines the Scheherazade-IF system, which uses crowdsourcing and artificial intelligence to automatically construct text-based interactive narrative experiences.
663	SimSensei Demonstration: A Perceptive Virtual Human Interviewer for Healthcare Applications	Louis-Philippe Morency, Giota Stratou, David DeVault, Arno Hartholt, Margo Lhommet, Gale Lucas, Fabrizio Morbini, Kallirroi Georgila, Stefan Scherer, Jonathan Gratch, Stacy Marsella, David Traum, Albert Rizzo	We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress.
664	LOL — Laugh Out Loud	Florian Pecune, Beatrice Biancardi, Yu Ding, Catherine Pelachaud, Maurizio Mancini, Giovanna Varni, Antonio Camurri, Gualtiero Volpe	Our aim is to study copying capabilitiesparticipate in enhancing user’s experience in the interaction.User listens to funny audio stimuli in the presenceof a laughing agent: when funniness of audio increases, theagent laughs and the quality of its body movement (directionand amplitude of laughter movements) is modulated on-theflyby user’s body features.
665	Using Social Relationships to Control Narrative Generation	Julie Porteous, Fred Charles, Marc Cavazza	In our work we have developed a plan-based approach to narrative generation that uses character relationships as a key determinant in controlling plan shape (relationships are key in genres such as serial dramas and soaps).
666	Interactive Narrative Planning in The Best Laid Plans	Stephen G. Ware, R. Michael Young, Christian Stith, Phillip Wright	The Best Laid Plans is an interactive narrative video game that uses cognitive-inspired fast planning techniques to generate stories with conflict during play.
667	Knowledge Representation and Reasoning: What’s Hot	Chitta Baral, Giuseppe De Giacomo	Knowledge Representation and Reasoning: What’s Hot
668	What’s Hot in Crowdsourcing and Human Computation	Jeffrey Bigham	What’s Hot in Crowdsourcing and Human Computation
669	BDDs Strike Back (in AI Planning)	Stefan Edelkamp, Peter Kissmann, Alvaro Torralba	In this paper we review the outcome of the competition, briefly looking into the internals of the competing systems.
670	What’s Hot in the SAT and ASP Competitions	Marijn Heule, Torsten Schaub	Here we present the highlights of the Satisfiability (SAT) and Answer Set Programming (ASP) competitions.
671	What Is Hot in CHI	Wei Li	What Is Hot in CHI
672	AIBIRDS: The Angry Birds Artificial Intelligence Competition	Jochen Renz	In this paper we describe why this is a very difficult problem, why it is a challenge for AI, and why it is an important step towards building AI that can successfully interact with the real world.
673	RoboCup@Home — Benchmarking Domestic Service Robots	Sven Wachsmuth, Dirk Holz, Maja Rudinac, Javier Ruiz-del-Solar	The RoboCup@Home league has been founded in 2006with the idea to drive research in AI and related fieldstowards autonomous and interactive robots that copewith real life tasks in supporting humans in everday life.The yearly competition format establishes benchmarkingas a continuous process with yearly changes insteadof a single challenge.
674	Data Science for Social Good — 2014 KDD Highlights	Wei Wang	Data Science for Social Good — 2014 KDD Highlights