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