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23rd UAI 2007: Vancouver, BC, Canada
- Ronald Parr, Linda C. van der Gaag:
UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada, July 19-22, 2007. AUAI Press 2007, ISBN 0-9749039-3-0 - Christopher Amato, Daniel S. Bernstein, Shlomo Zilberstein:
Optimizing Memory-Bounded Controllers for Decentralized POMDPs. 1-8 - Debarun Bhattacharjya, Ross D. Shachter:
Evaluating influence diagrams with decision circuits. 9-16 - Joseph Bockhorst, Nebojsa Jojic:
Discovering Patterns in Biological Sequences by Optimal Segmentation. 17-24 - Darius Braziunas, Craig Boutilier:
Minimax regret based elicitation of generalized additive utilities. 25-32 - Francois Caron, Manuel Davy, Arnaud Doucet:
Generalized Polya Urn for Time-varying Dirichlet Process Mixtures. 33-40 - Allen Chang, Eyal Amir:
Reachability Under Uncertainty. 41-48 - Yiling Chen, David M. Pennock:
A Utility Framework for Bounded-Loss Market Makers. 49-56 - Arthur Choi, Mark Chavira, Adnan Darwiche:
Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks. 57-66 - Pierre-Arnaud Coquelin, Rémi Munos:
Bandit Algorithms for Tree Search. 67-74 - Ethan W. Dereszynski, Thomas G. Dietterich:
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. 75-82 - Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer, Leslie Pack Kaelbling:
Learning Probabilistic Relational Dynamics for Multiple Tasks. 83-92 - Joshua V. Dillon, Yi Mao, Guy Lebanon, Jian Zhang:
Statistical Translation, Heat Kernels and Expected Distances. 93-100 - Daniel Eaton, Kevin P. Murphy:
Bayesian structure learning using dynamic programming and MCMC. 101-108 - Michael Eichler, Vanessa Didelez:
Causal Reasoning in Graphical Time Series Models. 109-116 - Ad Feelders:
A new parameter Learning Method for Bayesian Networks with Qualitative Influences. 117-124 - Lucie Galand, Patrice Perny:
Search for Choquet-optimal paths under uncertainty. 125-132 - Amir Globerson, Tommi S. Jaakkola:
Convergent Propagation Algorithms via Oriented Trees. 133-140 - Vibhav Gogate, Bozhena Bidyuk, Rina Dechter:
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality. 141-148 - Roger B. Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng:
Shift-Invariance Sparse Coding for Audio Classification. 149-158 - Gholamreza Haffari, Anoop Sarkar:
Analysis of Semi-Supervised Learning with the Yarowsky Algorithm. 159-166 - Firas Hamze, Nando de Freitas:
Large-Flip Importance Sampling. 167-174 - Michael P. Holmes, Alexander G. Gray, Charles L. Isbell Jr.:
Fast Nonparametric Conditional Density Estimation. 175-182 - Alexander Ihler:
Accuracy Bounds for Belief Propagation. 183-190 - Ariel Jaimovich, Ofer Meshi, Nir Friedman:
Template Based Inference in Symmetric Relational Markov Random Fields. 191-199 - Changsung Kang, Jin Tian:
Polynomial Constraints in Causal Bayesian Networks. 200-208 - Ashish Kapoor, Eric Horvitz:
On Discarding, Caching, and Recalling Samples in Active Learning. 209-216 - Lukas Kroc, Ashish Sabharwal, Bart Selman:
Survey Propagation Revisited. 217-226 - Manabu Kuroki, Zhihong Cai:
Evaluation of the Causal Effect of Control Plans in Nonrecursive Structural Equation Models. 227-234 - Eric Lantz, Soumya Ray, David Page:
Learning Bayesian Network Structure from Correlation-Immune Data. 235-242 - Wei Li, David M. Blei, Andrew McCallum:
Nonparametric Bayes Pachinko Allocation. 243-250 - Jennifer Listgarten, David Heckerman:
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach. 251-258 - Radu Marinescu, Rina Dechter:
Best-First AND/OR Search for Most Probable Explanations. 259-266 - Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney:
Collaborative Filtering and the Missing at Random Assumption. 267-275 - Robert Mateescu, Rina Dechter:
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical Models. 276-284 - Marina Meila, Kapil Phadnis, Arthur Patterson, Jeff A. Bilmes:
Consensus ranking under the exponential model. 285-294 - Gergely Neu, Csaba Szepesvári:
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods. 295-302 - José M. Peña:
Reading Dependencies from Polytree-Like Bayesian Networks. 303-309 - Roland Ramsahai:
Causal Bounds and Instruments. 310-317 - David S. Rosenberg, Dan Klein, Ben Taskar:
Mixture-of-Parents Maximum Entropy Markov Models. 318-325 - Suchi Saria, Uri Nodelman, Daphne Koller:
Reasoning at the Right Time Granularity. 326-334 - Purnamrita Sarkar, Andrew W. Moore:
A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs. 335-343 - Sven Seuken, Shlomo Zilberstein:
Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs. 344-351 - Ilya Shpitser, Judea Pearl:
What Counterfactuals Can Be Tested. 352-359 - Tomi Silander, Petri Kontkanen, Petri Myllymäki:
On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter. 360-367 - Parag Singla, Pedro M. Domingos:
Markov Logic in Infinite Domains. 368-375 - Charles Sutton, Andrew McCallum:
Improved Dynamic Schedules for Belief Propagation. 376-383 - Umar Syed, Robert E. Schapire:
Imitation Learning with a Value-Based Prior. 384-391 - Jin Tian:
A Criterion for Parameter Identification in Structural Equation Models. 392-399 - Yevgeniy Vorobeychik, Daniel M. Reeves, Michael P. Wellman:
Constrained Automated Mechanism Design for Infinite Games of Incomplete Information. 400-407 - Chenggang Wang, Roni Khardon:
Policy Iteration for Relational MDPs. 408-415 - Yair Weiss, Chen Yanover, Talya Meltzer:
MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies. 416-425 - Ydo Wexler, Dan Geiger:
Importance Sampling via Variational Optimization. 426-433 - Fusun Yaman, Marie desJardins:
More-or-Less CP-Networks. 434-441 - Liu Yang, Rong Jin, Rahul Sukthankar:
Bayesian Active Distance Metric Learning. 442-449 - Jiji Zhang:
A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables. 450-457 - Brian D. Ziebart, Anind K. Dey, James A. Bagnell:
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification. 458-465 - Or Zuk, Liat Ein-Dor, Eytan Domany:
Ranking Under Uncertainty. 466-473 - Moisés Goldszmidt:
Making life better one large system at a time: Challenges for UAI research. 475-481 - Marco Ramoni:
Statistical Mechanics of Biological Networks. 482-483
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