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ORCID24th UAI 2008: Helsinki, Finland
- David A. McAllester, Petri Myllymäki:
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 - 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 P. 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 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, Kallista A. 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 D. 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 B. Laber, Susan A. Murphy:
Small Sample Inference for Generalization Error in Classification Using the CUD Bound. 357-365 - Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis. 366-374 - Gregory Lawrence, Stuart Russell:
Improving Gradient Estimation by Incorporating Sensor Data. 375-382 - Daniel Lowd, Pedro M. 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 A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. 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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