24. UAI 2008: Helsinki, Finland

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

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