Table of Contents

• Adaptive inference on general graphical modelsUmut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu, Özgür Sümer. 1-8 [doi]
• Identifying reasoning patterns in gamesDimitrios Antos, Avi Pfeffer. 9-17 [doi]
• Learning Inclusion-Optimal Chordal GraphsVincent Auvray, Louis Wehenkel. 18-25 [doi]
• Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite MatricesDavid Barber. 26-33 [doi]
• Sensitivity analysis in decision circuitsDebarun Bhattacharjya, Ross D. Shachter. 34-42 [doi]
• Greedy Block Coordinate Descent for Large Scale Gaussian Process RegressionLiefeng Bo, Cristian Sminchisescu. 43-52 [doi]
• CORL: A Continuous-state Offset-dynamics Reinforcement LearnerEmma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy. 53-61 [doi]
• On Identifying Total Effects in the Presence of Latent Variables and Selection biasZhihong Cai, Manabu Kuroki. 62-69 [doi]
• Complexity of Inference in Graphical ModelsVenkat Chandrasekaran, Nathan Srebro, Prahladh Harsha. 70-78 [doi]
• Approximating the Partition Function by Deleting and then Correcting for Model EdgesArthur Choi, Adnan Darwiche. 79-87 [doi]
• Multi-View Learning over Structured and Non-Identical OutputsKuzman Ganchev, João Graça, John Blitzer, Ben Taskar. 88-96 [doi]
• Bounds on the Bethe Free Energy for Gaussian NetworksBotond Cseke, Tom Heskes. 97-104 [doi]
• Bayesian network learning by compiling to weighted MAX-SATJames Cussens. 105-112 [doi]
• Identifying Optimal Sequential DecisionsA. Philip Dawid, Vanessa Didelez. 113-120 [doi]
• Strategy Selection in Influence Diagrams using Imprecise ProbabilitiesCassio Polpo de Campos, Qiang Ji. 121-128 [doi]
• Sensitivity analysis for finite Markov chains in discrete timeGert de Cooman, Filip Hermans, Erik Quaeghebeur. 129-136 [doi]
• Learning Convex Inference of MarginalsJustin Domke. 137-144 [doi]
• Projected Subgradient Methods for Learning Sparse GaussiansJohn Duchi, Stephen Gould, Daphne Koller. 145-152 [doi]
• Knowledge Combination in Graphical Multiagent ModelsQuang Duong, Michael Wellman, Satinder Singh. 153-160 [doi]
• Almost Optimal Intervention Sets for Causal DiscoveryFrederick Eberhardt. 161-168 [doi]
• Gibbs Sampling in Factorized Continuous-Time Markov ProcessesTal El-Hay, Nir Friedman, Raz Kupferman. 169-178 [doi]
• Convex Point Estimation using Undirected Bayesian Transfer HierarchiesGal Elidan, Benjamin Packer, Geremy Heitz, Daphne Koller. 179-186 [doi]
• Learning and Solving Many-Player Games through a Cluster-Based RepresentationSevan G. Ficici, David C. Parkes, Avi Pfeffer. 187-195 [doi]
• Constrained Approximate Maximum Entropy Learning of Markov Random FieldsVarun Ganapathi, David Vickrey, John Duchi, Daphne Koller. 196-203 [doi]
• Multi-View Learning over Structured and Non-Identical OutputsKuzman Ganchev, João Graça, John Blitzer, Ben Taskar. 204-211 [doi]
• AND/OR Importance SamplingVibhav Gogate, Rina Dechter. 212-219 [doi]
• Church: a language for generative modelsNoah Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum. 220-229 [doi]
• Latent Topic Models for HypertextAmit Gruber, Michal Rosen-Zvi, Yair Weiss. 230-239 [doi]
• A Game-Theoretic Analysis of Updating Sets of ProbabilitiesPeter Grünwald, Joseph Y. Halpern. 240-247 [doi]
• Sampling First Order Logical ParticlesHannaneh Hajishirzi, Eyal Amir. 248-255 [doi]
• Sparse Stochastic Finite-State Controllers for POMDPsEric A. Hansen. 256-263 [doi]
• Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free EnergiesTamir Hazan, Amnon Shashua. 264-273 [doi]
• Learning When to Take Advice: A Statistical Test for Achieving A Correlated EquilibriumGreg Hines, Kate Larson. 274-281 [doi]
• Causal discovery of linear acyclic models with arbitrary distributionsPatrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph Ramsey, Gustavo Lacerda, Shohei Shimizu. 282-289 [doi]
• Cumulative distribution networks and the derivative-sum-product algorithmJim C. Huang, Brendan J. Frey. 290-297 [doi]
• Toward Experiential Utility Elicitation for Interface CustomizationBowen Hui, Craig Boutilier. 298-305 [doi]
• Speeding Up Planning in Markov Decision Processes via Automatically Constructed AbstractionAlejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner. 306-314 [doi]
• Bayesian Out-TreesTony Jebara. 315-324 [doi]
• Feature Selection via Block-Regularized RegressionSeyoung Kim, Eric P. Xing. 325-332 [doi]
• On Identifying Total Effects in the Presence of Latent Variables and Selection biasManabu Kuroki, Zhihong Cai. 333-340 [doi]
• Partitioned Linear Programming Approximations for MDPsBranislav Kveton, Milos Hauskrecht. 341-348 [doi]
• The Computational Complexity of Sensitivity Analysis and Parameter TuningJohan Kwisthout, Linda C. van der Gaag. 349-356 [doi]
• Small Sample Inference for Generalization Error in Classification Using the CUD BoundEric Laber, Susan Murphy. 357-365 [doi]
• Discovering Cyclic Causal Models by Independent Components AnalysisGustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer. 366-374 [doi]
• Improving Gradient Estimation by Incorporating Sensor DataGregory Lawrence, Stuart J. Russell. 375-382 [doi]
• Learning Arithmetic CircuitsDaniel Lowd, Pedro Domingos. 383-392 [doi]
• Estimation and clustering with infinite rankingsMarina Meila, Le Bao. 393-402 [doi]
• The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent FeaturesKurt T. Miller, Thomas L. Griffiths, Michael I. Jordan. 403-410 [doi]
• Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial RegressionDavid M. Mimno, Andrew McCallum. 411-418 [doi]
• A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic GamesEnrique Munoz de Cote, Michael L. Littman. 419-426 [doi]
• Explanation Trees for Causal Bayesian NetworksUlf H. Nielsen, Jean-Philippe Pellet, André Elisseeff. 427-434 [doi]
• On the Conditional Independence Implication Problem: A Lattice-Theoretic ApproachMathias Niepert, Dirk Van Gucht, Marc Gyssens. 435-443 [doi]
• Learning Hidden Markov Models for Regression using Path AggregationKeith Noto, Mark Craven. 444-451 [doi]
• Bounding Search Space Size via (Hyper)tree DecompositionsLars Otten, Rina Dechter. 452-459 [doi]
• Observation Subset Selection as Local Compilation of Performance ProfilesYan Radovilsky, Solomon Eyal Shimony. 460-467 [doi]
• Improving the Accuracy and Efficiency of MAP Inference for Markov LogicSebastian Riedel. 468-475 [doi]
• Model-Based Bayesian Reinforcement Learning in Large Structured DomainsStéphane Ross, Joelle Pineau. 476-483 [doi]
• CT-NOR: Representing and Reasoning About Events in Continuous TimeAleksandr Simma, Moisés Goldszmidt, John MacCormick, Paul Barham, Richard Black, Rebecca Isaacs, Richard Mortier. 484-493 [doi]
• Efficient Inference in Persistent Dynamic Bayesian NetworksTomás Singliar, Denver Dash. 494-502 [doi]
• Tightening LP Relaxations for MAP using Message PassingDavid Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss. 503-510 [doi]
• Learning the Bayesian Network Structure: Dirichlet Prior vs DataHarald Steck. 511-518 [doi]
• New Techniques for Algorithm Portfolio DesignMatthew J. Streeter, Stephen F. Smith. 519-527 [doi]
• Dyna-Style Planning with Linear Function Approximation and Prioritized SweepingRichard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael H. Bowling. 528-536 [doi]
• Flexible Priors for Exemplar-based ClusteringDaniel Tarlow, Richard S. Zemel, Brendan J. Frey. 537-545 [doi]
• Propagation using Chain Event GraphsPeter A. Thwaites, Jim Q. Smith, Robert G. Cowell. 546-553 [doi]
• Identifying Dynamic Sequential PlansJin Tian. 554-561 [doi]
• Hierarchical POMDP Controller Optimization by Likelihood MaximizationMarc Toussaint, Laurent Charlin, Pascal Poupart. 562-570 [doi]
• Modelling local and global phenomena with sparse Gaussian processesJarno Vanhatalo, Aki Vehtari. 571-578 [doi]
• Continuous Time Dynamic Topic ModelsChong Wang, David M. Blei, David Heckerman. 579-586 [doi]
• Hybrid Variational/Gibbs Collapsed Inference in Topic ModelsMax Welling, Yee Whye Teh, Bert Kappen. 587-594 [doi]
• Inference for Multiplicative ModelsYdo Wexler, Christopher Meek. 595-602 [doi]
• Refractor Importance SamplingHaohai Yu, Robert van Engelen. 603-611 [doi]