UAI'98: listing of accepted papers

Serafin Moral (smc@decsai.ugr.es)
Sun, 26 Apr 1998 21:10:04 +0200 (MET DST)

FOURTEENTH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE

This message contains a listing of the papers accepted for
presentation at the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI-98). Each paper will be presented
at a plenary or poster session. All papers will appear in the
proceedings of the conference.

For details about the conference, including on-line registration
and information about a full-day course on uncertain reasoning,
please visit the conference web page at the following URL:
http://www.uai98.cbmi.upmc.edu

*********************************************************************

On the Acceptability of Arguments in Preference-Based Argumentation
Leila Amgoud and Claudette Cayrol

Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework
Salem Benferhat and Claudio Sossai

A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian Networks and Its Complexity
Mark Bloemeke and Marco Valtorta

Structured Reachability Analysis for Markov Decision Processes
Craig Boutilier, Ronen I. Brafman, and Christopher Geib

Tractable Inference for Complex Stochastic Processes
Xavier Boyen and Daphne Koller

Empirical Analysis of Predictive Algorithms for Collaborative Filtering
John S. Breese, David Heckerman, and Carl Kadie

Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus
Luis M. de Campos, Juan M. Fernandez, and Juan F. Huete

Dealing with Uncertainty in Situation Assessment: Towards a Symbolic Approach
Charles Castel, Corine Cossart, and Catherine Tessier

Marginalizing in Undirected Graph and Hypergraph Models
Enrique F. Castillo, Juan Ferrandiz, and Pilar Sanmartin

Utility Elicitation as a Classification Problem
Urszula Chajewska, Lise Getoor, Joseph Norman, and Yuval Shahar

Irrelevance and Independence Relations in Quasi-Bayesian Networks
Fabio Cozman

Dynamic Join Trees
Adnan Darwiche

On the Semi-Markov Equivalence of Causal Models
Benoit Desjardins

Comparative Uncertainty, Belief Functions and Accepted Beliefs
Didier Dubois, Helene Fargier, and Henri Prade

Qualitative Decision Theory with Sugeno Integrals
Didier Dubois, Henri Prade, and Regis Sabbadin

The Bayesian Structural EM Algorithm
Nir Friedman

Learning the Structure of Dynamic Probabilistic Networks
Nir Friedman, Kevin Murphy, and Stuart Russell

Learning by Transduction
Alex Gammerman, V. Vovk, and Vladimir Vapnik

Graphical Models and Exponential Families
Dan Geiger and Christopher Meek

Psychological and Normative Theories of Causal Power and the Probabilities of Causes
Clark Glymour

Updating Sets of Probabilities
Adam J. Grove and Joseph Y. Halpern

Minimum Encoding Approaches for Predictive Modeling
Peter Grunwald, Petri Kontkanen, Petri Myllymaki, Tomi Silander, and Henry Tirri

Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures
Vu Ha and Peter Haddawy

Axiomatizing Causal Reasoning
Joseph Halpern

Solving POMDPs by Searching in Policy Space
Eric A. Hansen

Hierarchical Solution of Markov Decision Processes Using Macro-actions
Milos Hauskrecht, Nicolas Meuleau, Craig Boutilier, Leslie Pack Kaelbling, and Thomas Dean

A Bayesian Approach to Inferring a User's Needs from Free-Text Queries for Assistance
David Heckerman and Eric Horvitz

Evaluating Las Vegas Algorithms -- Pitfalls and Remedies
Holger H. Hoos and Thomas Stutzle

An Anytime Algorithm for Decision Making Under Uncertainty
Michael C. Horsch and David Poole

Lumiere: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
Eric Horvitz, Jack Breese, David Heckerman, David Hovel, and Koos Rommelse

Any Time Probabilistic Reasoning for Sensor Validation
Pablo H. Ibarguengoytia, L. Enrique Sucar, and Sunil Vadera

Measure Selection: Notions of Rationality and Representation Independence
Manfred Jaeger

Implementing Resolute Choice Under Uncertainty
Jean-Yves Jaffray

Dealing with Uncertainty on the Initial State of a Petri Net
Iman Jarkass and Michele Rombaut

Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: Approximation and Maximum Likelihood Estimation
Wenxin Jiang and Martin A. Tanner

Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks
Michael Kearns and Yishay Mansour

Large Deviation Methods for Approximate Probabilistic Inference, with Rates of Convergence
Michael Kearns and Lawrence Saul

Mixture Representations for Inference and Learning in Boltzmann Machines
Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan, and Tommi Jaakkola

A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions
Vasilica Lepar and Prakash P. Shenoy

Using Qualitative Relationships for Bounding Probability Distributions
Chao-Lin Liu and Michael P. Wellman

Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
Chao-Lin Liu and Michael P. Wellman

Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge
Thomas Lukasiewicz

Lazy Propagation in Junction Trees
Anders L. Madsen and Finn V. Jensen

Constructing Situation Specific Bayesian Networks
Suzanne M. Mahoney and Kathryn Blackmond Laskey

Treatment Choice in Heterogeneous Populations Using Experiments without Covariate Data (Invited Paper)
Charles F. Manski

An Experimental Comparison of Several Clustering Methods
Marina Meila and David Heckerman

>From Likelihood to Plausibility
Paul-Andre Monney

A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
Stefano Monti and Gregory F. Cooper

Resolving Conflicting Arguments under Uncertainties
Benson H.K. Ng, Kam-Fai Wong, and Boon-Toh Low

Flexible Decomposition Methods for Weakly Coupled Markov Decision Processes
Ronald Parr

Polylogarithmic Time Parallel Bayesian Inference
David M. Pennock

Learning from What You Don't Observe
Mark A. Peot and Ross D. Shachter

Context-Specific Approximation in Probabilistic Inference
David Poole

Approximation Algorithms for Probabilistic Decoding
Irina Rish, Kalev Kask and Rina Dechter

Decision Theoretic Foundations of Graphical Model Selection
Paola Sebastiani and Marco Ramoni

On the Geometry of Bayesian Graphical Models with Hidden Variables
Raffaella Settimi and Jim Q. Smith

Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)
Ross D. Shachter

Switching Portfolios
Yoram Singer

Bayesian Networks from the Point of View of Chain Graphs
Milan Studeny

Learning Mixtures of Bayesian Networks
Bo Thiesson, Christopher Meek, David Maxwell Chickering, and David Heckerman

Probabilistic Inference in Influence Diagrams
Nevin L. Zhang

Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method
Nevin L. Zhang and Stephen S. Lee

Flexible and Approximate Computation through State-Space Reduction
Weixiong Zhang