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
*