NOTE: Early registration deadline is Monday May 13, 2002.
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18th Conference on Uncertainty in AI
UAI-2002
Call for Participation
August 1-4, 2002
http://www.cs.ucla.edu/~uai02
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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 encourage submissions to UAI-2002 that report on
advances in these core areas, as well as those dealing with insights
derived from the construction and use of applications involving
reasoning under uncertainty.
The 18th Conference on Uncertainty in Artificial Intelligence will be
held in August, 2002 at the University of Alberta, Edmonton, Canada.
The main technical session will be on August 2-4, and will be preceded
with a tutorial program on August 1st. The UAI-2002 venue is in close
proximity to several other conferences, including KDD (July 23-25),
AAAI (July 28 - August 1), and ISMB (August 3-7).
For detailed information about the technical program, schedule, online
registration and accommodations please go to the conference web site at
http://www.cs.ucla.edu/~uai02
Conference Program
******************
The main technical program at UAI-2002 will be from Friday Aug. 2nd
until Sunday Aug. 4th. It will include 66 technical papers that were
selected after a peer-review process. Of these 26 will be given as
plenary presentations, and 40 as poster presentations. The list of
accepted papers is attached below.
The following invited speakers will be giving talks at UAI-2002:
* Persi Diaconis, Stanford University (Banquet speaker)
* Eric Grimson, MIT
* Rob Schapire, AT&T
* Sebastian Thrun, CMU
* Peter P. Wakker, Maastricht University
The conference will be preceded by a day of advanced tutorials on
Thursday Aug. 1st. This year we have four tutorials:
* Markov Decision Processes
Craig Boutilier, University of Toronto
* Statistical Methods in Natural Language Processing
Michael Collins, AT&T
* Randomization and Rational Decision Making in Optimization
Bart Selman & Carla Gomes, Cornell
* Uncertainty and Computational Markets
Mike Wellman, University of Michigan
Registration
************
Early registration deadline is May 13, 2002. To register online please
go to
http://www.cs.ucla.edu/~uai02/
and select the "Registration" option.
At the conference web site you can find additional information on the
conference location and accommodations.
Conference Organization
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Please direct general inquiries to the General Conference Chair at
koller@cs.stanford.edu. Inquiries about the conference program should
be directed to the Program Co-Chairs at uai02-pchairs@cs.ucla.edu.
General Program Chair:
* Daphne Koller, Stanford. koller@cs.stanford.edu
Program Co-Chairs:
* Adnan Darwiche, UCLA. darwiche@cs.ucla.edu
* Nir Friedman, Hebrew University. nir@cs.huji.ac.il
List of Accepted Papers
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Markov Equivalence Classes for Maximal Ancestral Graphs
Ayesha Ali, Thomas Richardson
Asymptotic Model Selection for Naive Bayesian Networks
Dmitry Rusakov, Dan Geiger
Dimension Correction for Hierarchical Latent Class Models
Tomas Kocka, Nevin L. Zhang
Formalizing Scenario Analysis
Peter McBurney, Simon Parsons
Expectation propagation for approximate inference in dynamic Bayesian
networks
Tom Heskes, Onno Zoeter
Finding Optimal Bayesian Networks
Max Chickering, Christopher Meek
Updating probabilities
Joseph Halpern, Peter Grunwald
Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
Adaptive Foreground and Shadow Detection in Image Sequences
yang Wang, tele Tan
On the Construction of the Inclusion Boundary Neighbourhood for Markov
Equivalence Classes of Bayesian Network Structures
Vincent Auvray, Louis Wehenkel
A-sequential Influence Diagrams
Finn V. Jensen, Marta Vomlelova
Bayesian Network Classifiers in a High Dimensional Framework
Tatjana Pavlenko, Dietrich von Rosen
Staged Mixture Modeling and Boosting
Christopher Meek, David Heckerman, Bo Thiesson
Mechanism Design with Execution Uncertainty
Ryan Porter, Yoav Shoham, Moshe Tennenholtz, Amir Ronen
Decayed MCMC Filtering
Bhaskara Marthi, Stuart Russell, Hanna Pasula, Yuval Peres
Introducing Variable Importance Tradeoffs into CP-Nets
Carmel Domshlak, Ronen Brafman
An MDP-Based Recommender System
Guy Shani, Ronen Brafman, David Heckerman
Exploiting functional dependence in Bayesian network inference with a
computerized adaptive test as an example
Jirka Vomlel
Improved Feature Selection by Mutual Information Distributions
Marco Zaffalon, Marcus Hutter
Reasoning about expectation
Joseph Halpern, Riccardo Pucella
A Constraint Satisfaction Approach to the Robust Spanning Tree with
Interval Data
Pascal Van Hentenryck, Ionut Aron
>From Qualitative to Quantitative Probabilistic Networks
Silja Renooij, Linda van der Gaag
IPF for discrete chain factor graphs
Wim Wiegerinck, Tom Heskes
A new class of upper bounds on the log partition function
Martin Wainwright, Tommi Jaakkola, Alan Willsky
Bipolar possibilistic representations
Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade
MAP Complexity Results and Approximation Methods
James Park
Statistical Decisions Using Likelihood Information Without Prior
Probabilities
Phan Giang, Prakash Shenoy
General Lower Bounds based on Computer Generated Higher Order Expansions
Martijn Leisink, Hilbert Kappen
Inference with Separately Specified Sets of Probabilities in Credal
Networks
Fabio Cozman, Jose Carlos Rocha
Efficient Nash Computation in Large Population Games with Bounded
Influence
Michael Kearns, Yishay Mansour
Planning Under Continuous Time and Resource Uncertainty: A Challenge for
AI
Nicolas Meuleau, John Bresina, Richard Dearden, Sailesh
Ramakrishnan, David Smith, Rich Washington
Almost-everywhere algorithmic stability and generalization error
Samuel Kutin, Partha Niyogi
Polynomial Value Iteration Algorithms for Deterministic MDPs
Omid Madani
A Bayesian Network Scoring Metric That Is Based on Globally Uniform
Parameter Priors
Mehmet Kayaalp, Greg Cooper
Complexity of Mechanism Design
Vincent Conitzer, Tuomas Sandholm
Value Function Approximation in Markov Games
Michail Lagoudakis, Ron Parr
Qualitative MDPs and POMDPs: An Order-of-Magnitude Approximation
Blai Bonet, Judea Pearl
An Information-Theoretic External Cluster-Validity Measure
Byron DOM
On the Testable Implications of Causal Models with Hidden Variables
Jin Tian, Judea Pearl
Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net
Uri Lerner, Brooks Moses, Maricia Scott, Sheila McIlraith, Daphne
Koller
Reinforcement Learning with Partially Known World Dynamics
Christian Shelton
Real-valued All-Dimensions search: Low-overhead rapid searching over
subsets of attributes
Andrew Moore, Jeff Schneider
Discriminative Probabilistic Models for Relational Data
Ben Taskar, Daphne Koller, Pieter Abbeel
Coordinate: Probabilistic Forecasting of Presence and Availability
Eric Horvitz, Carl Kadie, Paul Koch, Andy Jacobs
Inductive Policy Selection for First-Order Markov Decision Processes
Alan Fern, Bob Givan, Sung Wook Yoon
Continuous Time Bayesian Networks
Uri Nodelman, Daphne Koller, Christian Shelton
Learning Hierarchical Object-Based Maps of Non-Stationary Environments
Rahul Biswas, Daphne Koller, Benson Limketkai, Scott Sanner,
Sebastian Thrun, Dragomir Anguelov
Factored Particles for Scalable Monitoring
Leonid Peshkin, Avi Pfeffer, Brenda Ng
Multiagent Planning with Distributed Linear Programs
Carlos Guestrin, Geoffrey Gordon
CFW: A Collaborative Filtering System Using Posteriors Over Weights of
Evidence
Carl Kadie, David Heckerman, Christopher Meek
Anytime State-Based Solution Methods for Decision Processes with
non-Markovian Rewards
Sylvie Thiebaux, Froduald Kabanza, John Slaney
Modeling Information Incorporation in Markets, with Application to
Detecting and Explaining Events
David Pennock, Eric Glover, Sandip Debnath, Lee Giles
Reduction of Maximum Entropy Models to Hidden Markov Models
Joshua Goodman
Tree-dependent Component Analysis
Francis Bach, Michael Jordan
Generalized Instrumental Variables
Carlos Brito, Judea Pearl
Expectation-Propagation for the Generative Aspect Model
Tom Minka, John Lafferty
Unsupervised Active Learning in Large Domains
Harald Steck, Tommi Jaakkola
The Thing That We Tried Didn't Work Very Well: Deictic Representation
in Reinforcement Learning
Natalia Gardiol, Sarah Finney, Leslie Kaelbling, Tim Oates
Continuation Methods for Mixing Heterogeneous Sources
Adrian Corduneanu, Tommi Jaakkola
Loopy Belief Propagation and Gibbs Measures
Sekhar Tatikonda, Michael Jordan
Real-Time Inference with Large-Scale Temporal Bayes Nets
Masami Takikawa, Bruce D'Ambrosio, Ed Wright
Interpolating Conditional Density Trees
Scott Davies, Andrew Moore
Causes and Explanations in the Structural-Model Approach: Tractable
Cases
Thomas Lukasiewicz, Thomas Eiter
Learning with Scope With Application to Information Extraction and
Classification
David Blei, James Bagnell, Andrew McCallum
Optimal Time Bounds for Approximate Clustering
Greg Plaxton, Ramgopal Mettu
Iterative Join-Graph Propagation
Robert Mateescu, Rina Dechter, Kalev Kask
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