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17th UAI 2001: Seattle, Washington, USA
- Jack S. Breese, Daphne Koller:
UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2-5, 2001. Morgan Kaufmann 2001, ISBN 1-55860-800-1 - Kannan Achan, Brendan J. Frey, Ralf Koetter:
A Factorized Variational Technique for Phase Unwrapping in Markov Random Field. 1-6 - Eyal Amir:
Efficient Approximation for Triangulation of Minimum Treewidth. 7-15 - Nicos Angelopoulos, James Cussens:
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure. 16-23 - Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade:
Graphical readings of possibilistic logic bases. 24-31 - Hans L. Bodlaender, Arie M. C. A. Koster, Frank van den Eijkhof, Linda C. van der Gaag:
Pre-processing for Triangulation of Probabilistic Networks. 32-39 - Blai Bonet:
A Calculus for Causal Relevance. 40-47 - Blai Bonet:
Instrumentality Tests Revisited. 48-55 - Craig Boutilier, Fahiem Bacchus, Ronen I. Brafman:
UCP-Networks: A Directed Graphical Representation of Conditional Utilities. 56-64 - Hei Chan, Adnan Darwiche:
When do Numbers Really Matter? 65-74 - Jian Cheng, Marek J. Druzdzel:
Confidence Inference in Bayesian Networks. 75-82 - Tianjiao Chu, Richard Scheines, Peter Spirtes:
Semi-Instrumental Variables: A Test for Instrument Admissibility. 83-90 - Robert G. Cowell:
Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models. 91-97 - David Danks, Clark Glymour:
Linearity Properties of Bayes Nets with Binary Variables. 98-104 - Gary A. Davis:
Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions. 105-111 - Rina Dechter, David Larkin:
Hybrid Processing of Beliefs and Constraints. 112-119 - Nando de Freitas, Pedro A. d. F. R. Højen-Sørensen, Stuart Russell:
Variational MCMC. 120-127 - Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan:
Efficient Stepwise Selection in Decomposable Models. 128-135 - Tal El-Hay, Nir Friedman:
Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables. 136-143 - Gal Elidan, Nir Friedman:
Learning the Dimensionality of Hidden Variables. 144-151 - Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby:
Multivariate Information Bottleneck. 152-161 - Phan Hong Giang, Prakash P. Shenoy:
A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory. 162-170 - Steven B. Gillispie, Michael D. Perlman:
Enumerating Markov Equivalence Classes of Acyclic Digraph Models. 171-177 - Carlos Guestrin, Dirk Ormoneit:
Robust Combination of Local Controllers. 178-185 - Vu A. Ha, Peter Haddawy, John Miyamoto:
Similarity Measures on Preference Structures, Part II: Utility Functions. 186-193 - Joseph Y. Halpern, Judea Pearl:
Causes and Explanations: A Structural-Model Approach: Part 1: Causes. 194-202 - Joseph Y. Halpern, Riccardo Pucella:
A Logic for Reasoning about Upper Probabilities. 203-210 - Hiromitsu Hattori, Makoto Yokoo, Yuko Sakurai, Toramatsu Shintani:
A Dynamic Programming Model for Determining Bidding Strategies in Sequential Auctions: Quasi-linear Utility and Budget Constraints. 211-218 - Milos Hauskrecht, Eli Upfal:
A Clustering Approach to Solving Large Stochastic Matching Problems. 219-226 - Geoffrey E. Hinton, Yee Whye Teh:
Discovering Multiple Constraints that are Frequently Approximately Satisfied. 227-234 - Eric Horvitz, Yongshao Ruan, Carla P. Gomes, Henry A. Kautz, Bart Selman, David Maxwell Chickering:
A Bayesian Approach to Tackling Hard Computational Problems. 235-244 - Geoff A. Jarrad:
Estimating Well-Performing Bayesian Networks using Bernoulli Mixtures. 245-252 - Michael J. Kearns, Michael L. Littman, Satinder Singh:
Graphical Models for Game Theory. 253-260 - Tomás Kocka, Remco R. Bouckaert, Milan Studený:
On characterizing Inclusion of Bayesian Networks. 261-268 - Tomás Kocka, Robert Castelo:
Improved learning of Bayesian networks. 269-276 - Petri Kontkanen, Petri Myllymäki, Henry Tirri:
Classifier Learning with Supervised Marginal Likelihood. 277-284 - Jérôme Lang, Philippe Muller:
Plausible reasoning from spatial observations. 285-292 - John D. Lafferty, Larry A. Wasserman:
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk. 293-300 - Kathryn B. Laskey, Suzanne M. Mahoney, Ed Wright:
Hypothesis Management in Situation-Specific Network Construction. 301-309 - Uri Lerner, Ronald Parr:
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms. 310-318 - Uri Lerner, Eran Segal, Daphne Koller:
Exact Inference in Networks with Discrete Children of Continuous Parents. 319-328 - Thomas Lukasiewicz:
Probabilistic Logic Programming under Inheritance with Overriding. 329-336 - Anders L. Madsen, Dennis Nilsson:
Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation. 337-345 - Dimitris Margaritis, Sebastian Thrun:
A Bayesian Multiresolution Independence Test for Continuous Variables. 346-353 - Pedrito Maynard-Reid II, Urszula Chajewska:
Aggregating Learned Probabilistic Beliefs. 354-361 - Thomas P. Minka:
Expectation Propagation for approximate Bayesian inference. 362-369 - Quaid Morris:
Recognition Networks for Approximate Inference in BN20 Networks. 370-377 - Kevin P. Murphy, Yair Weiss:
The Factored Frontier Algorithm for Approximate Inference in DBNs. 378-385 - Ann E. Nicholson, Tal Boneh, Tim A. Wilkin, Kaye Stacey, Liz Sonenberg, Vicki Steinle:
A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application. 386-394 - Dirk Ormoneit, Christiane Lemieux, David J. Fleet:
Lattice Particle Filters. 395-402 - James D. Park, Adnan Darwiche:
Approximating MAP using Local Search. 403-410 - Judea Pearl:
Direct and Indirect Effects. 411-420 - Avi Pfeffer:
Sufficiency, Separability and Temporal Probabilistic Models. 421-428 - Daniel Pless, George F. Luger:
Toward General Analysis of Recursive Probability Models. 429-436 - Alexandrin Popescul, Lyle H. Ungar, David M. Pennock, Steve Lawrence:
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. 437-444 - Pascal Poupart, Craig Boutilier:
Vector-space Analysis of Belief-state Approximation for POMDPs. 445-452 - Pascal Poupart, Luis E. Ortiz, Craig Boutilier:
Value-Directed Sampling Methods for POMDPs. 453-461 - Christopher Raphael:
A Mixed Graphical Model for Rhythmic Parsing. 462-471 - Khashayar Rohanimanesh, Sridhar Mahadevan:
Decision-Theoretic Planning with Concurrent Temporally Extended Actions. 472-479 - Paat Rusmevichientong, Benjamin Van Roy:
A Tractable POMDP for Dynamic Sequencing with Applications to Personalized Internet Content Provision. 480-487 - Rita Sharma, David Poole:
Symmetric Collaborative Filtering Using the Noisy Sensor Model. 488-495 - Christian R. Shelton:
Policy Improvement for POMDPs Using Normalized Importance Sampling. 496-503 - Nathan Srebro:
Maximum Likelihood Bounded Tree-Width Markov Networks. 504-511 - Jin Tian, Judea Pearl:
Causal Discovery from Changes. 512-521 - Tim Van Allen, Russell Greiner, Peter Hooper:
Bayesian Error-Bars for Belief Net Inference. 522-529 - Linda C. van der Gaag, Silja Renooij:
Analysing Sensitivity Data from Probabilistic Networks. 530-537 - Lex Weaver, Nigel Tao:
The Optimal Reward Baseline for Gradient-Based Reinforcement Learning. 538-545 - Jacob A. Wegelin, Thomas S. Richardson:
Cross-covariance modelling via DAGs with hidden variables. 546-553 - Max Welling, Yee Whye Teh:
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation. 554-561 - Steve J. Young:
Statistical Modeling in Continuous Speech Recognition (CSR). 562-571 - Bo Zhang, Qingsheng Cai, Jianfeng Mao, Baining Guo:
Planning and Acting under Uncertainty: A New Model for Spoken Dialogue System. 572-579 - Andrew Zimdars, David Maxwell Chickering, Christopher Meek:
Using Temporal Data for Making Recommendations. 580-588
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