17. UAI 2001: Seattle, Washington, USA

• 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 J. 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 P. 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 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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