UAI-2000: The Sixteenth Conference on Uncertainty in Artificial Intelligence

Accepted Papers

All papers will be presented in plenary or poster sessions. The schedule of paper presentations is listed in The Technical Program (Schedule).

A Complete Calculus for Possibilistic Logic Programming with Fuzzy Propositional Variables
Teresa Alsinet, Lluís Godo

Reversible Jump MCMC Simulated Annealing for Neural Networks
Christophe Andrieu, Nando de Freitas, Arnaud Doucet

Perfect Tree-Like Markovian Distributions
Ann Becker, Dan Geiger, Chris Meek

The Complexity of Decentralized Control of Markov Decision Processes
Daniel Bernstein, Shlomo Zilberstein, Neil Immerman

Markov Chains of Bayesian Multinets
Jeff Bilmes

Variational Relevance Vector Machines
Christopher Bishop, Michael Tipping

Approximately Optimal Monitoring of Plan Preconditions
Craig Boutilier

Utilities as Random Variables: Density Estimation and Structure Discovery
Urszula Chajewska, Daphne Koller

Computational Investigation of Low-Discrepancy Sequences in Bayesian Networks
Jian Cheng, Marek Druzdzel

A Decision Theoretic Approach to Targeted Advertising
Max Chickering, David Heckerman

Bayesian Classification and Feature Selection from Finite Data Sets
Frans Coetzee, Steve Lawrence, C. Lee Giles

A Bayesian Method for Causal Modeling and Discovery Under Selection
Gregory Cooper

Separation Properties of Sets of Probability Measures
Fabio Cozman

"Stochastic logic programs: sampling, inference and applications "
James Cussens

A Differential Approach to Inference in Bayesian Networks
Adnan Darwiche

Any-Space Probabilistic Inference
Adnan Darwiche

Experiments with random projection
Sanjoy Dasgupta

A two-round variant of EM for Gaussian mixtures
Sanjoy Dasgupta, Leonard Schulman

Minimum Message Length Clustering Using Gibbs Sampling
Ian Davidson

Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed Discrete And Continuous Variables
Scott Davies, Andrew Moore

Rao-Blackwellised Filtering for Dynamic Bayesian Networks
Arnaud Doucet, Nando de Freitas, Kevin Murphy, Stuart Russell

Learning Graphical Models of Images, Videos and Their Spatial Transformations
Brendan Frey, Nebojsa Jojic

Efficient Likelihood Computations Using Value Abstractions
Nir Friedman, Dan Geiger, Noam Lotner

Being Bayesian about Network Structure
Nir Friedman, Daphne Koller

Gaussian Process Networks
Nir Friedman, Iftach Nachman

A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
Phan Giang, Prakash P. Shenoy

Building a Stochastic Dynamic Model of Application Use
Peter Gorniak, David Poole

Maximum Entropy and the Glasses You Are Looking Through
Peter Grunwald

Conditional Plausibility Measures and Bayesian Networks
Joseph Halpern

Inference for Belief Networks Using Coupling From the Past
Michael Harvey, Radford Neal

Dependency Networks for Density Estimation, Collaborative Filtering, and Data Visualization
David Heckerman, Max Chickering, Chris Meek, Robert Rounthwaite, Carl Kadie

YGGDRASIL - A Statistical Package for Learning Split Models
Søren Højsgaard

Probabilistic Arc Consistency: A connection between constraint reasoning and probabilistic reasoning
Michael Horsch, Bill Havens

Feature selection and dualities in maximum entropy discrimination
Tony Jebara, Tommi Jaakola

Marginalization in Composed Probabilistic Models
Radim Jirousek

A postulate-based analysis of merging operations in possibilistic logic
Souhila Kaci, Salem Benferhat, Didier Dubois, Henri Prade

Fast Planning in Stochastic Games
Michael Kearns, Yishay Mansour, Satinder Singh

Making Sensitivity Analysis Computationally Efficient
Uffe Kjærulff, Linda C. van der Gaag

Policy Iteration for Factored MDPs
Daphne Koller, Ron Parr

Game Networks
Pierfrancesco La Mura

Combinatorial optimization by learning and simulation of Bayesian
Pedro Larrañaga, Ramon Etxeberria, Jose A. Lozano, Jose M. Peña

Causal Mechanism-based Model Construction
Tsai-Ching Lu, Marek Druzdzel, Tze-Yun Leong

Credal Networks under Maximum Entropy
Thomas Lukasiewicz

Risk agoras: Dialectical argumentation for scientific reasoning
Peter McBurney, Simon Parsons

Tractable Bayesian Learning of Tree Belief Networks
Marina Meila, Tommi Jaakola

Probabilistic Models for Agents' Beliefs and Decisions
Brian Milch, Daphne Koller

The Anchors Hierachy: Using the triangle inequality to survive high dimensional data
Andrew Moore

PEGASUS: A policy search method for large MDPs and POMDPs
Andrew Ng, Mike Jordan

Representing and solving asymmetric Bayesian decision problems
Thomas D. Nielsen, Finn Jensen

Using ROBDDs for inference in Bayesian networks with troubleshooting as an example
Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Jensen, Uffe Kjærulff

Evaluating Influence Diagrams using LIMIDs
Dennis Nilsson, Steffen Lauritzen

Adaptive Importance Sampling for Estimation in Structured Domains
Luis E Ortiz, Leslie Kaelbling

Conversation as Action Under Uncertainty
Tim Paek, Eric Horvitz

Probabilistic Models for Query Approximation with Large Sparse Binary Datasets
Dmitry Pavlov, Heikki Mannila, Padhraic Smyth

Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
David Pennock, Eric Horvitz, Steve Lawrence, C. Lee Giles

Compact Securities Markets for Pareto Optimal Reallocation of Risk
David Pennock, Michael Wellman

Learning to Cooperate via Policy Search
Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau, Leslie Kaelbling

Value-Directed Belief State Approximation for POMDPs
Pascal Poupart, Craig Boutilier

Probabilistic State-Dependent Grammars for Plan Recognition
David Pynadath, Michael Wellman

Pivotal Pruning of Trade-offs in QPNs
Silja Renooij, Linda C. van der Gaag, Simon Parsons, Shaw Green

Monte Carlo inference via greedy importance sampling
Dale Schuurmans, Finnegan Southey

Combining Feature and Prototype Pruning by Uncertainty Minimization
Marc Sebban, Richard Nock

Nash Convergence of Gradient Dynamics in Iterated General-Sum Games
Satinder Singh, Michael Kearns, Yishay Mansour

A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters
Claus Skaanning

On the Use of Skeletons when Learning in Bayesian Networks
Harald Steck

Dynamic Trees: A structured variational method giving efficient propagation rules
Amos Storkey

An uncertainty framework for classification
Loo-Nin Teow, Kia-Fock Loe

A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
Jin Tian

Probabilities of Causation: Bounds and Identification
Jin Tian, Judea Pearl

Model-Based Hierarchical Clustering
Shivakumar Vaithyanathan, Byron Dom

Conditional Independence and Markov Properties in Possibility Theory
Jirina Vejnarova

User Interface Tools for Navigation in Conditional Probability Tables and Graphical Elicitation of Probabilities in Bayesian Networks
Haiqin Wang, Marek Druzdzel

Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
Wim Wiegerinck

Model Criticism of Bayesian Networks with Latent Variables
David Williamson, Russell Almond, Robert Mislevy

Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
Frank Wittig, Anthony Jameson