18. UAI 2002: Edmonton, Alberta, Canada

• Ayesha R. Ali, Thomas Richardson:
  Markov Equivalence Classes for Maximal Ancestral Graphs. 1-9
  
• Dragomir Anguelov, Rahul Biswas, Daphne Koller, Benson Limketkai, Sebastian Thrun:
  Learning Hierarchical Object Maps of Non-Stationary Environments with Mobile Robots. 10-17
  
• Ionut D. Aron, Pascal Van Hentenryck:
  A Constraint Satisfaction Approach to the Robust Spanning Tree Problem with Interval Data. 18-25
  
• Vincent Auvray, Louis Wehenkel:
  On the Construction of the Inclusion Boundary Neighbourhood for Markov Equivalence Classes of Bayesian Network Structures. 26-35
  
• Francis R. Bach, Michael I. Jordan:
  Tree-dependent Component Analysis. 36-44
  
• Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade:
  Bipolar Possibilistic Representations. 45-52
  
• David M. Blei, J. Andrew Bagnell, Andrew McCallum:
  Learning with Scope, with Application to Information Extraction and Classification. 53-60
  
• Blai Bonet, Judea Pearl:
  Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation. 61-68
  
• Ronen I. Brafman, Carmel Domshlak:
  Introducing Variable Importance Tradeoffs into CP-Nets. 69-76
  
• John L. Bresina, Richard Dearden, Nicolas Meuleau, Sailesh Ramkrishnan, David E. Smith, Richard Washington:
  Planning under Continuous Time and Resource Uncertainty: A Challenge for AI. 77-84
  
• Carlos Brito, Judea Pearl:
  Generalized Instrumental Variables. 85-93
  
• David Maxwell Chickering, Christopher Meek:
  Finding Optimal Bayesian Networks. 94-102
  
• Vincent Conitzer, Tuomas Sandholm:
  Complexity of Mechanism Design. 103-110
  
• Adrian Corduneanu, Tommi Jaakkola:
  Continuation Methods for Mixing Heterogenous Sources. 111-118
  
• Scott Davies, Andrew W. Moore:
  Interpolating Conditional Density Trees. 119-127
  
• Rina Dechter, Kalev Kask, Robert Mateescu:
  Iterative Join-Graph Propagation. 128-136
  
• Byron Dom:
  An Information-Theoretic External Cluster-Validity Measure. 137-145
  
• Thomas Eiter, Thomas Lukasiewicz:
  Causes and Explanations in the Structural-Model Approach : Tractable Cases. 146-153
  
• Sarah Finney, Peter P. Wakker, Leslie Pack Kaelbling, Tim Oates:
  The Thing that we Tried Didn't Work very Well: Deictic Representation in Reinforcement Learning. 154-161
  
• Dan Geiger, Christopher Meek, Bernd Sturmfels:
  Factorization of Discrete Probability Distributions. 162-169
  
• Phan Hong Giang, Prakash P. Shenoy:
  Statistical Decisions Using Likelihood Information Without Prior Probabilities. 170-178
  
• Joshua Goodman:
  Reduction of Maximum Entropy Models to Hidden Markov Models. 179-186
  
• Peter Grünwald, Joseph Y. Halpern:
  Updating Probabilities. 187-196
  
• Carlos Guestrin, Geoffrey J. Gordon:
  Distributed Planning in Hierarchical Factored MDPs. 197-206
  
• Joseph Y. Halpern, Riccardo Pucella:
  Reasoning about Expectation. 207-215
  
• Tom Heskes, Onno Zoeter:
  Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. 216-223
  
• Eric Horvitz, Paul Koch, Carl Myers Kadie, Andy Jacobs:
  Coordinates: Probabilistic Forecasting of Presence and Availability. 224-233
  
• Finn Verner Jensen, Marta Vomlelová:
  Unconstrained Influence Diagrams. 234-241
  
• Carl Myers Kadie, Christopher Meek, David Heckerman:
  CFW: A Collaborative Filtering System Using Posteriors over Weights of Evidence. 242-250
  
• Mehmet Kayaalp, Gregory F. Cooper:
  A Bayesian Network Scoring Metic that Is Based on Globally Uniform Parameter Priors. 251-258
  
• Michael J. Kearns, Yishay Mansour:
  Efficient Nash Computation in Large Population Games with Bounded Influence. 259-266
  
• Tomás Kocka, Nevin Lianwen Zhang:
  Dimension Correction for Hierarchical Latent Class Models. 267-274
  
• Samuel Kutin, Partha Niyogi:
  Almost-everywhere Algorithmic Stability and Generalization Error. 275-282
  
• Michail G. Lagoudakis, Ronald Parr:
  Value Function Approximation in Zero-Sum Markov Games. 283-292
  
• Martijn A. R. Leisink, Hilbert J. Kappen:
  General Lower Bounds based on Computer Generated Higher Order Expansions. 293-300
  
• Uri Lerner, Brooks Moses, Scott Maricia, Sheila A. McIlraith, Daphne Koller:
  Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net. 301-310
  
• Omid Madani:
  Polynomial Value Iteration Algorithms for Detrerminstic MDPs. 311-318
  
• Bhaskara Marthi, Hanna Pasula, Stuart J. Russell, Yuval Peres:
  Decayed MCMC Filtering. 319-326
  
• Peter McBurney, Simon Parsons:
  Formalizing Scenario Analysis. 327-334
  
• Christopher Meek, Bo Thiesson, David Heckerman:
  Staged Mixture Modelling and Boosting. 335-343
  
• Ramgopal R. Mettu, C. Greg Plaxton:
  Optimal Time Bounds for Approximate Clustering. 344-351
  
• Thomas P. Minka, John D. Lafferty:
  Expectation-Propogation for the Generative Aspect Model. 352-359
  
• Andrew W. Moore, Jeff G. Schneider:
  Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes. 360-369
  
• Brenda Ng, Leonid Peshkin, Avi Pfeffer:
  Factored Particles for Scalable Monitoring. 370-377
  
• Uri Nodelman, Christian R. Shelton, Daphne Koller:
  Continuous Time Bayesian Networks. 378-387
  
• James D. Park:
  MAP Complexity Results and Approximation Methods. 388-396
  
• Tatjana Pavlenko, Dietrich von Rosen:
  Bayesian Network Classifiers in a High Dimensional Framework. 397-404
  
• David M. Pennock, Sandip Debnath, Eric J. Glover, C. Lee Giles:
  Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Events. 405-413
  
• Ryan Porter, Amir Ronen, Yoav Shoham, Moshe Tennenholtz:
  Mechanism Design with Execution Uncertainty. 414-421
  
• Silja Renooij, Linda C. van der Gaag:
  From Qualitative to Quantitative Probabilistic Networks. 422-429
  
• José Carlos Ferreira da Rocha, Fabio Gagliardi Cozman:
  Inference with Seperately Specified Sets of Probabilities in Credal Networks. 430-437
  
• Dmitry Rusakov, Dan Geiger:
  Asymptotic Model Selection for Naive Bayesian Networks. 438-445
  
• Robert E. Schapire:
  Advances in Boosting. 446-452
  
• Guy Shani, Ronen I. Brafman, David Heckerman:
  An MDP-based Recommender System. 453-460
  
• Christian R. Shelton:
  Reinforcement Learning with Partially Known World Dynamics. 461-468
  
• Harald Steck, Tommi Jaakkola:
  Unsupervised Active Learning in Large Domains. 469-476
  
• Masami Takikawa, Bruce D'Ambrosio, Ed Wright:
  Real-Time Inference with Large-Scale Temporal Bayes Nets. 477-484
  
• Benjamin Taskar, Pieter Abbeel, Daphne Koller:
  Discriminative Probabilistic Models for Relational Data. 485-492
  
• Sekhar Tatikonda, Michael I. Jordan:
  Loopy Belief Propogation and Gibbs Measures. 493-500
  
• Sylvie Thiébaux, Froduald Kabanza, John K. Slaney:
  Anytime State-Based Solution Methods for Decision Processes with non-Markovian Rewards. 501-510
  
• Sebastian Thrun:
  Particle Filters in Robotics. 511-518
  
• Jin Tian, Judea Pearl:
  On the Testable Implications of Causal Models with Hidden Variables. 519-527
  
• Jirí Vomlel:
  Exploiting Functional Dependence in Bayesian Network Inference. 528-535
  
• Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky:
  A New Class of upper Bounds on the Log Partition Function. 536-543
  
• Peter P. Wakker:
  Decision Principles to Justify Carnap's Updating Method and to Suggest Corrections. 544-551
  
• Yang Wang, Tele Tan:
  Adaptive Foreground and Shadow Detection in Image Sequences. 552-559
  
• Wim Wiegerinck, Tom Heskes:
  IPF for Discrete Chain Factor Graphs. 560-567
  
• Sung Wook Yoon, Alan Fern, Robert Givan:
  Inductive Policy Selection for First-Order MDPs. 568-576
  
• Marco Zaffalon, Marcus Hutter:
  Robust Feature Selection by Mutual Information Distributions. 577-584
  

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