NOTE: Early registration deadline has been extended to Monday June 9, 2003.
********************************************************************** 19th Conference on Uncertainty in AI (UAI-2003) CALL FOR PARTICIPATION August 7-10, 2003 Hyatt Hotel, Acapulco, Mexico http://research.microsoft.com/uai2003/ ********************************************************************** Since 1985, the Conference on Uncertainty in Artificial Intelligence (UAI) has been the primary international forum for presenting new results on the use of principled methods for reasoning under uncertainty within intelligent systems. The scope of UAI is wide, including, but not limited to, representation, automated reasoning, learning, decision making and knowledge acquisition under uncertainty. We have encouraged submissions to UAI-2003 that report on theoretical or methodological advances in representation, automated reasoning, learning, decision making and knowledge acquisition under uncertainty, as well as submissions that report on systems that utilize techniques from these core areas. The main technical session will be on August 8-10, and will be preceded with an advanced tutorial program on August 7. The UAI-2003 is collocated with and immediately precedes the International Joint Conference on Artificial Intelligence (IJCAI) which will be held August 9-15. For detailed information about the technical program, schedule, online registration and accommodations please go to the conference web site at http://research.microsoft.com/uai2003/. Conference Program ****************** The main technical program at UAI-2003 will include 77 technical papers that were selected after a peer-review process. 25 of these will be given as plenary presentations, and 52 as poster presentations. The list of accepted papers is attached below. Invited Talks ************* The following invited speakers will be giving talks at UAI-2003: * Banquet talk Adrian F.M. Smith, University of London * Inferring 3D People from 2D Images Michael J. Black, Brown University * Strategic Reasoning and Graphical Models Michael Kearns, University of Pennsylvania * What's New in Statistical Machine Translation Kevin Knight, USC Information Sciences Institute * Some Measures of Incoherence: How not to gamble if you must Teddy Seidenfeld, Carnegie Mellon University Tutorials ********* The conference will be preceded by a day of advanced tutorials on Thursday August 7. This year we have four tutorials: * Graphical Model Research in Speech and Language Processing Jeff A. Bilmes, University of Washington * Probabilistic Models for Relational Domains Daphne Koller, Stanford University * Bayesian Networks for Forensic Identification Problems Steffen L. Lauritzen, Aalborg University * Uncertainty and Computational Markets Mike Wellman, University of Michigan Registration ************ Early registration deadline is June 9, 2003. To register online please go to http://research.microsoft.com/uai2003/ and select the "Registration" option. At the conference web site you can find additional information on the conference location and accommodations. Conference Organization *********************** Please direct general inquiries to the General Conference Chair at [EMAIL PROTECTED] Inquiries about the conference program should be directed to the Program Co-Chairs at [EMAIL PROTECTED] General Program Chair: * Adnan Darwiche, University of California, Los Angles. <[EMAIL PROTECTED]> Program Co-Chairs: * Uffe Kjaerulff, Aalborg University. <[EMAIL PROTECTED]> * Chris Meek, Microsoft Research. <[EMAIL PROTECTED]> List of Accepted Papers *********************** A Linear Belief Function Approach to Portfolio Evaluation Liping Liu, Catherine Shenoy, Prakash Shenoy Policy-contingent abstraction for robust robot control Joelle Pineau, Geoff Gordon, Sebastian Thrun The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach Benjamin Stewart, Jonathan Ko, Dieter Fox, Kurt Konolige Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards Charles Gretton, David Price, Sylvie Thiebaux The Information Bottleneck EM IB-EM Algorithm Gal Elidan, Nir Friedman On revising fuzzy belief bases Richard Booth, Eva Richter Learning Module Networks Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman Learning Continuous Time Bayesian Networks Uri Nodelman, Christian Shelton, Daphne Koller 1 Billion Pages = 1 Million Dollars? Mining the Web to Play ``Who Wants to be a Millionaire?'' Shyong Lam, David Pennock, Dan Cosley, Steve Lawrence Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, HongJian Zhang Marginalizing Out Future Passengers in Group Elevator Control Daniel Nikovski, Matthew Brand Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation Craig Boutilier, Rajarshi Das, Jeffrey Kephart, Gerry Tesauro, William Walsh Renewal Strings for cleaning astronomical databases Amos Storkey, Nigel Hambly, Christopher Williams, Bob Mann Approximate Decomposition: A Method for Bounding and Estimating Probabilistic and Deterministic Queries David Larkin Loopy Belief Propagation as a Basis for Communication in Sensor Networks Christopher Crick, Avi Pfeffer Efficient Gradient Estimation for Motor Control Learning Gregory Lawrence, Noah Cowan, Stuart Russell Large-Sample Learning of Bayesian Networks is Hard Max Chickering, David Heckerman, Christopher Meek Efficiently Inducing Features of Conditional Random Fields Andrew McCallum A generalized mean field algorithm for variational inference in exponential families Eric Xing, Michael I. Jordan, Stuart Russell A Logic for Reasoning about Evidence Joe Halpern, Riccardo Pucella On Information Regularization Adrian Corduneanu, Tommi Jaakkola An Empirical Study of w-Cutset Sampling for Bayesian Networks Bozhena Bidyuk, Rina Dechter Approximate inference and constrained optimization Tom Heskes, Kees Albers, Bert Kappen Upgrading Ambiguous Signs in QPNs Janneke Bolt, Silja Renooij, Linda van der Gaag A Tractable Probabilistic Model for Projection Pursuit Max Welling, Richard Zemel, Geoffrey Hinton A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence Mathias Drton, Thomas Richardson Stochastic complexity of Bayesian networks Keisuke Yamazaki, Sumio Watanabe On Local Optima in Learning Bayesian Networks Jens Dalgaard Nielsen, Tomas Kocka, Jose Manuel Peña A Distance-Based Branch and Bound Feature Selection Algorithm Ari Frank, Dan Geiger, Zohar Yakhini Decision Making with Partially Consonant Belief Functions Phan H. Giang, Prakash Shenoy Robust Independence Testing for Constraint-Based Learning of Causal Structure Denver Dash, Marek Druzdzel CLP(BN): Constraint Logic Programming for Probabilistic Knowledge Santos Costa Vitor, David Page, James Cussens, Maleeha Qazi Efficient Inference in Large Discrete Domains Rita Sharma, David Poole Solving MAP Exactly using Systematic Search James Park, Adnan Darwiche Dealing with uncertainty in fuzzy inductive reasoning methodology Francisco Mugica, Angela Nebot, Pilar Gomez LAYERWIDTH: Analysis of a New Metric for Directed Acyclic Graphs Mark Hopkins Strong Faithfulness and Uniform Consistency in Causal Inference Jiji Zhang, Peter Spirtes Locally Weighted Naive Bayes Eibe Frank, Mark Hall, Bernhard Pfahringer Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference Yong Gao On the Convergence of Bound Optimization Algorithms Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani Structure-Based Causes and Explanations in the Independent Choice Logic Alberto Finzi, Thomas Lukasiewicz Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models Dmitry Rusakov, Dan Geiger An Axiomatic Approach to Robustness in Search Problems with Multiple Scenarios Patrice Perny, Olivier Spanjaard Learning Riemannian Metrics Guy Lebanon Exploiting Locality in Searching the Web Joel Young, Thomas Dean Preference-based Graphic Models for Collaborative Filtering Rong Jin, Luo Si, Chengxiang Zhai Monte Carlo Matrix Inversion Policy Evaluation Fletcher Lu, Dale Schuurmans Bayesian Hierarchical Mixtures of Experts Markus Svensen, Christopher Bishop Toward a possibilistic handling of partially ordered information Sylvain Lagrue, Salem Benferhat, Odile Papini Incremental Compilation of Bayesian networks Julia Flores, Jose Gamez, Kristian G. Olesen Decentralized Sensor Fusion With Distributed Particle Filters Matthew Rosencrantz, Geoff Gordon, Sebastian Thrun Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories Thomas Eiter, Thomas Lukasiewicz Parametric Dependability Analysis through Probabilistic Horn Abduction Luigi Portinale, Andrea Bobbio, Stefania Montani New Advances in Inference by Recursive Conditioning David Allen, Adnan Darwiche Updating with incomplete observations Gert De Cooman, Marco Zaffalon An Importance Sampling Algorithm Based on Evidence Pre-propagation Changhe Yuan, Marek Druzdzel Boltzmann Machine Learning with the Latent Maximum Entropy Principle Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao Inference in Polytrees with Sets of Probabilities José Carlos Rocha, Fabio Cozman Reasoning about Bayesian Network Classifiers Hei Chan, Adnan Darwiche Using the structure of d-connecting paths as a qualitative measure of the strength of dependence Sanjay Chaudhuri, Thomas Richardson Active Collaborative Filtering Craig Boutilier, Richard Zemel, Benjamin Marlin Learning Generative Models of Similarity Matrices Romer Rosales, Brendan Frey Symbolic Generalization for On-line Planning Zhengzhu Feng, Eric Hansen, Shlomo Zilberstein Factor Graphs: A Unification of Directed and Undirected Graphical Models Brendan Frey Sufficient Dimensionality Reduction with Side Information Amir Globerson, Gal Chechik, Naftali Tishby Markov Random Walk Representations with Continuous Distributions Chen-Hsiang Yeang, Martin Szummer Monte-Carlo optimizations for resource allocation problems in stochastic networks Milos Hauskrecht, Tomas Singliar Systematic vs. Non-systematic Algorithms for Solving the MPE Task Radu Marinescu, Kalev Kask, Rina Dechter Probabilistic models for joint clustering and time-warping of multidimensional curves Darya Chudova, Scott Gaffney, Padhraic Smyth Practically Perfect Christopher Meek, Max Chickering Optimal Limited Contingency Planning Nicolas Meuleau, David Smith Learning Measurement Models for Unobserved Variables Ricardo Silva, Richard Scheines, Clark Glymour, Peter Spirtes Budgeted Learning, Part II: The Naive-Bayes Case Daniel Lizotte, Omid Madani, Russell Greiner Value Elimination: Bayesian Inference via Backtracking Search Fahiem Bacchus, Shannon Dalmao, Toniann Pitassi A Simple Insight into Properties of Iterative Belief Propagation Rina Dechter, Robert Mateescu A Decision Making Perspective on Web Question Answering David Azari, Eric Horvitz, Susan Dumais, Eric Brill On Triangulating Dynamic Graphical Models Jeff Bilmes, Chris Bartels **********************************************************************