24. UAI 2008: Helsinki, Finland
David A. McAllester, Petri Myllymäki (Eds.): UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, Helsinki, Finland, July 9-12, 2008. AUAI Press 2008 ISBN 0-9749039-4-9- Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu, Özgür Sümer: Adaptive inference on general graphical models. 1-8
- David Barber: Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices. 26-33
- 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
- James Cussens: Bayesian network learning by compiling to weighted MAX-SAT. 105-112
- 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
- Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum: Church: a language for generative models. 220-229
- Peter Grünwald, Joseph Y. Halpern: A Game-Theoretic Analysis of Updating Sets of Probabilities. 240-247
- 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
- Manabu Kuroki, Zhihong Cai: On Identifying Total Effects in the Presence of Latent Variables and Selection bias. 333-340
- Johan Kwisthout, Linda C. van der Gaag: The Computational Complexity of Sensitivity Analysis and Parameter Tuning. 349-356
- Eric B. 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
- 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
- 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
- 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
- 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
- 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
- Max Welling, Yee Whye Teh, Bert Kappen: Hybrid Variational/Gibbs Collapsed Inference in Topic Models. 587-594
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last updated on 2013-06-18T211851 by the dblp team