éhtml>Uncertainty in Artificial Intelligence 2007 23. UAI 2007:
Vancouver,
BC,
Canada Ronald Parr , Linda C. van der Gaag (Eds.):
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.
1-8Debarun Bhattacharjya , Ross D. Shachter :
Evaluating influence diagrams with decision circuits.
9-16Joseph Bockhorst , Nebojsa Jojic :
Discovering Patterns in Biological Sequences by Optimal Segmentation.
17-24Darius Braziunas , Craig Boutilier :
Minimax regret based elicitation of generalized additive utilities.
25-32Francois Caron , Manuel Davy , Arnaud Doucet :
Generalized Polya Urn for Time-varying Dirichlet Process Mixtures.
33-40Allen Chang , Eyal Amir :
Reachability Under Uncertainty.
41-48Yiling Chen , David M. Pennock :
A Utility Framework for Bounded-Loss Market Makers.
49-56Arthur Choi , Mark Chavira , Adnan Darwiche :
Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks.
57-66Pierre-Arnaud Coquelin , Rémi Munos :
Bandit Algorithms for Tree Search.
67-74Ethan W. Dereszynski , Thomas G. Dietterich :
Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams.
75-82Ashwin Deshpande , Brian Milch , Luke S. Zettlemoyer , Leslie Pack Kaelbling :
Learning Probabilistic Relational Dynamics for Multiple Tasks.
83-92Joshua V. Dillon , Yi Mao , Guy Lebanon , Jian Zhang :
Statistical Translation, Heat Kernels and Expected Distances.
93-100Daniel Eaton , Kevin P. Murphy :
Bayesian structure learning using dynamic programming and MCMC.
101-108Michael Eichler , Vanessa Didelez :
Causal Reasoning in Graphical Time Series Models.
109-116Ad Feelders :
A new parameter Learning Method for Bayesian Networks with Qualitative Influences.
117-124Lucie Galand , Patrice Perny :
Search for Choquet-optimal paths under uncertainty.
125-132Amir Globerson , Tommi Jaakkola :
Convergent Propagation Algorithms via Oriented Trees.
133-140Vibhav Gogate , Bozhena Bidyuk , Rina Dechter :
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality.
141-148Roger Grosse , Rajat Raina , Helen Kwong , Andrew Y. Ng :
Shift-Invariance Sparse Coding for Audio Classification.
149-158Gholamreza Haffari , Anoop Sarkar :
Analysis of Semi-Supervised Learning with the Yarowsky Algorithm.
159-166Firas Hamze , Nando de Freitas :
Large-Flip Importance Sampling.
167-174Michael P. Holmes , Alexander G. Gray , Charles L. Isbell :
Fast Nonparametric Conditional Density Estimation.
175-182Alexander T. Ihler :
Accuracy Bounds for Belief Propagation.
183-190Ariel Jaimovich , Ofer Meshi , Nir Friedman :
Template Based Inference in Symmetric Relational Markov Random Fields.
191-199Changsung Kang , Jin Tian :
Polynomial Constraints in Causal Bayesian Networks.
200-208Ashish Kapoor , Eric Horvitz :
On Discarding, Caching, and Recalling Samples in Active Learning.
209-216Lukas Kroc , Ashish Sabharwal , Bart Selman :
Survey Propagation Revisited.
217-226Manabu Kuroki , Zhihong Cai :
Evaluation of the Causal Effect of Control Plans in Nonrecursive Structural Equation Models.
227-234Eric Lantz , Soumya Ray , David Page :
Learning Bayesian Network Structure from Correlation-Immune Data.
235-242Wei Li , David M. Blei , Andrew McCallum :
Nonparametric Bayes Pachinko Allocation.
243-250Jennifer Listgarten , David Heckerman :
Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach.
251-258Radu Marinescu , Rina Dechter :
Best-First AND/OR Search for Most Probable Explanations.
259-266Benjamin M. Marlin , Richard S. Zemel , Sam T. Roweis , Malcolm Slaney :
Collaborative Filtering and the Missing at Random Assumption.
267-275Robert Mateescu , Rina Dechter :
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical Models.
276-284Marina Meila , Kapil Phadnis , Arthur Patterson , Jeff A. Bilmes :
Consensus ranking under the exponential model.
285-294Gergely Neu , Csaba Szepesvári :
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods.
295-302Jose M. Peña :
Reading Dependencies from Polytree-Like Bayesian Networks.
303-309Roland Ramsahai :
Causal Bounds and Instruments.
310-317David S. Rosenberg , Dan Klein , Ben Taskar :
Mixture-of-Parents Maximum Entropy Markov Models.
318-325Suchi Saria , Uri Nodelman , Daphne Koller :
Reasoning at the Right Time Granularity.
326-334Purnamrita Sarkar , Andrew W. Moore :
A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs.
335-343Sven Seuken , Shlomo Zilberstein :
Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs.
344-351Ilya Shpitser , Judea Pearl :
What Counterfactuals Can Be Tested.
352-359Tomi Silander , Petri Kontkanen , Petri Myllymäki :
On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter.
360-367Parag Singla , Pedro Domingos :
Markov Logic in Infinite Domains.
368-375Charles A. Sutton , Andrew McCallum :
Improved Dynamic Schedules for Belief Propagation.
376-383Umar Syed , Robert E. Schapire :
Imitation Learning with a Value-Based Prior.
384-391Jin Tian :
A Criterion for Parameter Identification in Structural Equation Models.
392-399Yevgeniy Vorobeychik , Daniel M. Reeves , Michael P. Wellman :
Constrained Automated Mechanism Design for Infinite Games of Incomplete Information.
400-407Chenggang Wang , Roni Khardon :
Policy Iteration for Relational MDPs.
408-415Yair Weiss , Chen Yanover , Talya Meltzer :
MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies.
416-425Ydo Wexler , Dan Geiger :
Importance Sampling via Variational Optimization.
426-433Fusun Yaman , Marie desJardins :
More-or-Less CP-Networks.
434-441Liu Yang , Rong Jin , Rahul Sukthankar :
Bayesian Active Distance Metric Learning.
442-449Jiji Zhang :
A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables.
450-457Brian D. Ziebart , Anind K. Dey , James A. Bagnell :
Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification.
458-465Or Zuk , Liat Ein-Dor , Eytan Domany :
Ranking Under Uncertainty.
466-473Moisés Goldszmidt :
Making life better one large system at a time: Challenges for UAI research.
475-481Marco Ramoni :
Statistical Mechanics of Biological Networks.
482-483
Last update Fri May 25 08:43:40 2012
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