Table of Contents

• Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian ProcessesRyan Prescott Adams, George E. Dahl, Iain Murray. 1-9 [doi]
• Gaussian Process Topic ModelsAmrudin Agovic, Arindam Banerjee. 10-19 [doi]
• Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text StreamAmr Ahmed, Eric P. Xing. 20-29 [doi]
• Gibbs Sampling in Open-Universe Stochastic LanguagesNimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart J. Russell. 30-39 [doi]
• Compiling Possibilistic Networks: Alternative Approaches to Possibilistic InferenceRaouia Ayachi, Nahla Ben Amor, Salem Benferhat, Rolf Haenni. 40-47 [doi]
• Possibilistic Answer Set Programming RevisitedKim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir. 48-55 [doi]
• Three new sensitivity analysis methods for influence diagramsDebarun Bhattacharjya, Ross D. Shachter. 56-64 [doi]
• Bayesian Rose TreesCharles Blundell, Yee Whye Teh, Katherine A. Heller. 65-72 [doi]
• Probabilistic Similarity LogicMatthias Bröcheler, Lilyana Mihalkova, Lise Getoor. 73-82 [doi]
• RAPID: A Reachable Anytime Planner for Imprecisely-sensed DomainsEmma Brunskill, Stuart Russell. 83-92 [doi]
• ALARMS: Alerting and Reasoning Management System for Next Generation Aircraft HazardsAlan Carlin, Nathan Schurr, Janusz Marecki. 93-100 [doi]
• An Online Learning-based Framework for TrackingKamalika Chaudhuri, Yoav Freund, Daniel Hsu. 101-108 [doi]
• Super-Samples from Kernel HerdingYutian Chen, Max Welling, Alex J. Smola. 109-116 [doi]
• Prediction with Advice of Unknown Number of ExpertsAlexey V. Chernov, Vladimir Vovk. 117-125 [doi]
• Lifted Inference for Relational Continuous ModelsJaesik Choi, Eyal Amir, David J. Hill. 126-134 [doi]
• Distribution over Beliefs for Memory Bounded Dec-POMDP PlanningGabriel Corona, François Charpillet. 135-142 [doi]
• Inferring deterministic causal relationsPovilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf. 143-150 [doi]
• Inference-less Density Estimation using Copula Bayesian NetworksGal Elidan. 151-159 [doi]
• A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local ApproximationTom Erez, William D. Smart. 160-167 [doi]
• Playing games against nature: optimal policies for renewable resource allocationStefano Ermon, Jon Conrad, Carla P. Gomes, Bart Selman. 168-176 [doi]
• Maximum likelihood fitting of acyclic directed mixed graphs to binary dataRobin J. Evans, Thomas S. Richardson. 177-184 [doi]
• Learning Game Representations from Data Using Rationality ConstraintsXi Gao, Avi Pfeffer. 185-192 [doi]
• Identifying Causal Effects with Computer AlgebraLuis Garcia, Sarah Spielvogel, Seth Sullivant. 193-200 [doi]
• Real-Time Scheduling via Reinforcement LearningRobert Glaubius, Terry Tidwell, Christopher D. Gill, William D. Smart. 201-209 [doi]
• Formula-Based Probabilistic InferenceVibhav Gogate, Pedro Domingos. 210-219 [doi]
• Regularized Maximum Likelihood for Intrinsic Dimension EstimationMithun Das Gupta, Thomas S. Huang. 220-227 [doi]
• MDPs with UnawarenessJoseph Y. Halpern, Nan Rong, Ashutosh Saxena. 228-235 [doi]
• Intracluster Moves for Constrained Discrete-Space MCMCFiras Hamze, Nando de Freitas. 236-243 [doi]
• Robust Metric Learning by Smooth OptimizationKaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu. 244-251 [doi]
• The Hierarchical Dirichlet Process Hidden Semi-Markov ModelMatthew Johnson, Alan S. Willsky. 252-259 [doi]
• Combining Spatial and Telemetric Features for Learning Animal Movement ModelsBerk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick. 260-267 [doi]
• BEEM : Bucket Elimination with External MemoryKalev Kask, Rina Dechter, Andrew Gelfand. 268-276 [doi]
• Causal Conclusions that Flip Repeatedly and Their JustificationKevin T. Kelly, Conor Mayo-Wilson. 277-285 [doi]
• Bayesian exponential family projections for coupled data sourcesArto Klami, Seppo Virtanen, Samuel Kaski. 286-293 [doi]
• Anytime Planning for Decentralized POMDPs using Expectation MaximizationAkshat Kumar, Shlomo Zilberstein. 294-301 [doi]
• Robust LogitBoost and Adaptive Base Class (ABC) LogitBoostPing Li 0001. 302-311 [doi]
• Approximating Higher-Order Distances Using Random ProjectionsPing Li 0001, Michael W. Mahoney, Yiyuan She. 312-321 [doi]
• Solving Hybrid Influence Diagrams with Deterministic VariablesYijing Li, Prakash P. Shenoy. 322-331 [doi]
• Negative Tree Reweighted Belief PropagationQiang Liu, Alexander T. Ihler. 332-339 [doi]
• GraphLab: A New Framework For Parallel Machine LearningYucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein. 340-349 [doi]
• Parameter-Free Spectral Kernel LearningQi Mao, Ivor W. Tsang. 350-357 [doi]
• Dirichlet Process Mixtures of Generalized Mallows ModelsMarina Meila, Harr Chen. 358-367 [doi]
• Parametric Return Density Estimation for Reinforcement LearningTetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka. 368-375 [doi]
• Automated Planning in Repeated Adversarial GamesEnrique Munoz de Cote, Archie C. Chapman, Adam M. Sykulski, Nicholas R. Jennings. 376-383 [doi]
• A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic NetworksMathias Niepert. 384-391 [doi]
• Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented WalkerFarheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Yungjen Tung, Allen Caine. 392-400 [doi]
• Algorithms and Complexity Results for Exact Bayesian Structure LearningSebastian Ordyniak, Stefan Szeider. 401-408 [doi]
• The Cost of Troubleshooting Cost Clusters with Inside InformationThorsten J. Ottosen, Finn Verner Jensen. 409-416 [doi]
• On a Class of Bias-Amplifying Variables that Endanger Effect EstimatesJudea Pearl. 417-424 [doi]
• On Measurement Bias in Causal InferenceJudea Pearl. 425-432 [doi]
• Confounding Equivalence in Causal InferenceJudea Pearl, Azaria Paz. 433-441 [doi]
• A Family of Computationally E cient and Simple Estimators for Unnormalized Statistical ModelsMiika Pihlaja, Michael Gutmann, Aapo Hyvärinen. 442-449 [doi]
• Sparse-posterior Gaussian Processes for general likelihoodsYuan (Alan) Qi, Ahmed H. Abdel-Gawad, Thomas P. Minka. 450-457 [doi]
• Merging Knowledge Bases in Possibilistic Logic by Lexicographic AggregationGuilin Qi, Jianfeng Du, Weiru Liu, David A. Bell. 458-465 [doi]
• Characterizing the Set of Coherent Lower Previsions with a Finite Number of Constraints or VerticesErik Quaeghebeur. 466-473 [doi]
• Understanding Sampling Style Adversarial Search MethodsRaghuram Ramanujan, Ashish Sabharwal, Bart Selman. 474-483 [doi]
• Irregular-Time Bayesian NetworksMichael Ramati, Yuval Shahar. 484-491 [doi]
• Inference by Minimizing Size, Divergence, or their SumSebastian Riedel, David A. Smith, Andrew McCallum. 492-499 [doi]
• Convergent and Correct Message Passing Schemes for Optimization Problems over Graphical ModelsNicholas Ruozzi, Sekhar Tatikonda. 500 [doi]
• Exact and Approximate Inference in Associative Hierarchical Networks using Graph CutsChristopher Russell, Lubor Ladicky, Pushmeet Kohli, Philip H. S. Torr. 501-508 [doi]
• Dynamic programming in in uence diagrams with decision circuitsRoss D. Shachter, Debarun Bhattacharjya. 509-516 [doi]
• Maximizing the Spread of Cascades Using Network DesignDaniel Sheldon, Bistra N. Dilkina, Adam N. Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla P. Gomes, David B. Shmoys, William Allen, Ole Amundsen, William Vaughan. 517-526 [doi]
• On the Validity of Covariate Adjustment for Estimating Causal EffectsIlya Shpitser, Tyler J. VanderWeele, James M. Robins. 527-536 [doi]
• Gaussian Process Structural Equation Models with Latent VariablesRicardo Silva, Robert B. Gramacy. 537-545 [doi]
• Modeling Events with Cascades of Poisson ProcessesAleksandr Simma, Michael I. Jordan. 546-555 [doi]
• A Bayesian Matrix Factorization Model for Relational DataAjit Singh, Geoffrey J. Gordon. 556-563 [doi]
• Variance-Based Rewards for Approximate Bayesian Reinforcement LearningJonathan Sorg, Satinder P. Singh, Richard L. Lewis. 564-571 [doi]
• Matrix Coherence and the Nystrom MethodAmeet Talwalkar, Afshin Rostamizadeh. 572-579 [doi]
• Bayesian Inference in Monte-Carlo Tree SearchGerald Tesauro, V. T. Rajan, Richard Segal. 580-588 [doi]
• Bayesian Model Averaging Using the k-best Bayesian Network StructuresJin Tian, Ru He, Lavanya Ram. 589-597 [doi]
• Learning networks determined by the ratio of prior and dataMaomi Ueno. 598-605 [doi]
• Online Semi-Supervised Learning on Quantized GraphsMichal Valko, Branislav Kveton, Ling Huang, Daniel Ting. 606-614 [doi]
• Risk Sensitive Path Integral ControlBart van den Broek, Wim Wiegerinck, Hilbert J. Kappen. 615-622 [doi]
• Speeding up the binary Gaussian process classificationJarno Vanhatalo, Aki Vehtari. 623-631 [doi]
• Efficient Clustering with Limited Distance InformationKonstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia. 632-640 [doi]
• Learning Why Things Change: The Difference-Based Causality LearnerMark Voortman, Denver Dash, Marek J. Druzdzel. 641-650 [doi]
• Primal View on Belief PropagationTomás Werner. 651-657 [doi]
• Truthful Feedback for Sanctioning Reputation MechanismsJens Witkowski. 658-665 [doi]
• Rollout Sampling Policy Iteration for Decentralized POMDPsFeng Wu, Shlomo Zilberstein, Xiaoping Chen. 666-673 [doi]
• Modeling Multiple Annotator Expertise in the Semi-Supervised Learning ScenarioYan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy. 674-682 [doi]
• Hybrid Generative/Discriminative Learning for Automatic Image AnnotationShuang-Hong Yang, Jiang Bian, Hongyuan Zha. 683-690 [doi]
• Solving Multistage Influence Diagrams using Branch-and-Bound SearchChanghe Yuan, XiaoJian Wu, Eric A. Hansen. 691-700 [doi]
• Learning Structural Changes of Gaussian Graphical Models in Controlled ExperimentsBai Zhang, Yue Joseph Wang. 701-708 [doi]
• Source Separation and Higher-Order Causal Analysis of MEG and EEGKun Zhang, Aapo Hyvärinen. 709-716 [doi]
• Invariant Gaussian Process Latent Variable Models and Application in Causal DiscoveryKun Zhang, Bernhard Schölkopf, Dominik Janzing. 717-724 [doi]
• Multi-Domain Collaborative FilteringYu Zhang, Bin Cao, Dit-Yan Yeung. 725-732 [doi]
• A Convex Formulation for Learning Task Relationships in Multi-Task LearningYu Zhang, Dit-Yan Yeung. 733-442 [doi]
• Automatic Tuning of Interactive Perception ApplicationsQian Zhu, Branislav Kveton, Lily B. Mummert, Padmanabhan Pillai. 743-751 [doi]