UAI-2003: Call For Participation

uai03-pchairs at uai03-pchairs at
Wed Jun 4 03:10:03 EST 2003

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


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

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


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


Early registration deadline is June 9, 2003. To register online please go to

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 darwiche at Inquiries about the conference program should be directed to the Program Co-Chairs at uai03-pchairs at

General Program Chair:

* Adnan Darwiche, University of California, Los Angles. <darwiche at>

Program Co-Chairs:

* Uffe Kjaerulff, Aalborg University. <uk at>

* Chris Meek, Microsoft Research. <meek at>

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



More information about the Biomatrx mailing list