24. UAI 2008:
Helsinki,
Finland
David A. McAllester, Petri Myllymäki (Eds.):
UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, July 9-12, 2008, Helsinki, Finland.
AUAI Press 2008, ISBN 0-9749039-4-9 BibTeX
- Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu, Özgür Sümer:
Adaptive inference on general graphical models.
1-8
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- Dimitrios Antos, Avi Pfeffer:
Identifying reasoning patterns in games.
9-17
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- Vincent Auvray, Louis Wehenkel:
Learning Inclusion-Optimal Chordal Graphs.
18-25
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- David Barber:
Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices.
26-33
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- Debarun Bhattacharjya, Ross D. Shachter:
Sensitivity analysis in decision circuits.
34-42
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- Liefeng Bo, Cristian Sminchisescu:
Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression.
43-52
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- Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy:
CORL: A Continuous-state Offset-dynamics Reinforcement Learner.
53-61
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- Zhihong Cai, Manabu Kuroki:
On Identifying Total Effects in the Presence of Latent Variables and Selection bias.
62-69
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- Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha:
Complexity of Inference in Graphical Models.
70-78
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- Arthur Choi, Adnan Darwiche:
Approximating the Partition Function by Deleting and then Correcting for Model Edges.
79-87
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- Kuzman Ganchev, João Graça, John Blitzer, Ben Taskar:
Multi-View Learning over Structured and Non-Identical Outputs.
88-96
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- Botond Cseke, Tom Heskes:
Bounds on the Bethe Free Energy for Gaussian Networks.
97-104
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- James Cussens:
Bayesian network learning by compiling to weighted MAX-SAT.
105-112
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- A. Philip Dawid, Vanessa Didelez:
Identifying Optimal Sequential Decisions.
113-120
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- Cassio Polpo de Campos, Qiang Ji:
Strategy Selection in Influence Diagrams using Imprecise Probabilities.
121-128
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- Gert De Cooman, Filip Hermans, Erik Quaeghebeur:
Sensitivity analysis for finite Markov chains in discrete time.
129-136
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- Justin Domke:
Learning Convex Inference of Marginals.
137-144
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- John Duchi, Stephen Gould, Daphne Koller:
Projected Subgradient Methods for Learning Sparse Gaussians.
145-152
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- Quang Duong, Michael Wellman, Satinder Singh:
Knowledge Combination in Graphical Multiagent Models.
153-160
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- Frederick Eberhardt:
Almost Optimal Intervention Sets for Causal Discovery.
161-168
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- Tal El-Hay, Nir Friedman, Raz Kupferman:
Gibbs Sampling in Factorized Continuous-Time Markov Processes.
169-178
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- Gal Elidan, Benjamin Packer, Geremy Heitz, Daphne Koller:
Convex Point Estimation using Undirected Bayesian Transfer Hierarchies.
179-186
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- Sevan G. Ficici, David C. Parkes, Avi Pfeffer:
Learning and Solving Many-Player Games through a Cluster-Based Representation.
187-195
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- Varun Ganapathi, David Vickrey, John Duchi, Daphne Koller:
Constrained Approximate Maximum Entropy Learning of Markov Random Fields.
196-203
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- Kuzman Ganchev, João Graça, John Blitzer, Ben Taskar:
Multi-View Learning over Structured and Non-Identical Outputs.
204-211
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- Vibhav Gogate, Rina Dechter:
AND/OR Importance Sampling.
212-219
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- Noah Goodman, Vikash K. Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models.
220-229
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- Amit Gruber, Michal Rosen-Zvi, Yair Weiss:
Latent Topic Models for Hypertext.
230-239
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- Peter Grünwald, Joseph Y. Halpern:
A Game-Theoretic Analysis of Updating Sets of Probabilities.
240-247
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- Hannaneh Hajishirzi, Eyal Amir:
Sampling First Order Logical Particles.
248-255
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- Eric A. Hansen:
Sparse Stochastic Finite-State Controllers for POMDPs.
256-263
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- Tamir Hazan, Amnon Shashua:
Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies.
264-273
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- Greg Hines, Kate Larson:
Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium.
274-281
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- 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
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- Jim C. Huang, Brendan J. Frey:
Cumulative distribution networks and the derivative-sum-product algorithm.
290-297
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- Bowen Hui, Craig Boutilier:
Toward Experiential Utility Elicitation for Interface Customization.
298-305
Electronic Edition (link) BibTeX
- Alejandro Isaza, Csaba Szepesvári, Vadim Bulitko, Russell Greiner:
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstraction.
306-314
Electronic Edition (link) BibTeX
- Tony Jebara:
Bayesian Out-Trees.
315-324
Electronic Edition (link) BibTeX
- Seyoung Kim, Eric Xing:
Feature Selection via Block-Regularized Regression.
325-332
Electronic Edition (link) BibTeX
- Manabu Kuroki, Zhihong Cai:
On Identifying Total Effects in the Presence of Latent Variables and Selection bias.
333-340
Electronic Edition (link) BibTeX
- Branislav Kveton, Milos Hauskrecht:
Partitioned Linear Programming Approximations for MDPs.
341-348
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- Johan Kwisthout, Linda C. van der Gaag:
The Computational Complexity of Sensitivity Analysis and Parameter Tuning.
349-356
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- Eric Laber, Susan Murphy:
Small Sample Inference for Generalization Error in Classification Using the CUD Bound.
357-365
Electronic Edition (link) BibTeX
- Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis.
366-374
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- Gregory Lawrence, Stuart J. Russell:
Improving Gradient Estimation by Incorporating Sensor Data.
375-382
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- Daniel Lowd, Pedro Domingos:
Learning Arithmetic Circuits.
383-392
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- Marina Meila, Le Bao:
Estimation and clustering with infinite rankings.
393-402
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- Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan:
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features.
403-410
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- David M. Mimno, Andrew McCallum:
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression.
411-418
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- Enrique Munoz de Cote, Michael L. Littman:
A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games.
419-426
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- Ulf H. Nielsen, Jean-Philippe Pellet, André Elisseeff:
Explanation Trees for Causal Bayesian Networks.
427-434
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- Mathias Niepert, Dirk Van Gucht, Marc Gyssens:
On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach.
435-443
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- Keith Noto, Mark Craven:
Learning Hidden Markov Models for Regression using Path Aggregation.
444-451
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- Lars Otten, Rina Dechter:
Bounding Search Space Size via (Hyper)tree Decompositions.
452-459
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- Yan Radovilsky, Solomon Eyal Shimony:
Observation Subset Selection as Local Compilation of Performance Profiles.
460-467
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- Sebastian Riedel:
Improving the Accuracy and Efficiency of MAP Inference for Markov Logic.
468-475
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- Stéphane Ross, Joelle Pineau:
Model-Based Bayesian Reinforcement Learning in Large Structured Domains.
476-483
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- 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
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- Tomás Singliar, Denver Dash:
Efficient Inference in Persistent Dynamic Bayesian Networks.
494-502
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- David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing.
503-510
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- Harald Steck:
Learning the Bayesian Network Structure: Dirichlet Prior vs Data.
511-518
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- Matthew J. Streeter, Stephen F. Smith:
New Techniques for Algorithm Portfolio Design.
519-527
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- Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael H. Bowling:
Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping.
528-536
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- Daniel Tarlow, Richard S. Zemel, Brendan J. Frey:
Flexible Priors for Exemplar-based Clustering.
537-545
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- Peter A. Thwaites, Jim Q. Smith, Robert G. Cowell:
Propagation using Chain Event Graphs.
546-553
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- Jin Tian:
Identifying Dynamic Sequential Plans.
554-561
Electronic Edition (link) BibTeX
- Marc Toussaint, Laurent Charlin, Pascal Poupart:
Hierarchical POMDP Controller Optimization by Likelihood Maximization.
562-570
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- Jarno Vanhatalo, Aki Vehtari:
Modelling local and global phenomena with sparse Gaussian processes.
571-578
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- Chong Wang, David M. Blei, David Heckerman:
Continuous Time Dynamic Topic Models.
579-586
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- Max Welling, Yee Whye Teh, Bert Kappen:
Hybrid Variational/Gibbs Collapsed Inference in Topic Models.
587-594
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- Ydo Wexler, Christopher Meek:
Inference for Multiplicative Models.
595-602
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- Haohai Yu, Robert van Engelen:
Refractor Importance Sampling.
603-611
Electronic Edition (link) BibTeX
Copyright © Sat May 16 23:44:07 2009
by Michael Ley (ley@uni-trier.de)
