11th Conference on Uncertainty in AI, August 1995
David Poole
poole at cs.ubc.ca
Fri Jul 21 09:56:01 EDT 1995
The Conferences in Uncartianty in AI are the premier forum for work on
reasoning under uncertainty (including probabilistic and other
formalisms for uncertainty, representations for uncertainty, such as
Bayesian networks, algorithms for inference under uncertainty and learning
under ucertainty).
The 11th Conference on Uncertianty in AI will be held in Montreal,
18-20 August 1995 (just before IJCAI-95). For full details including
registration information and an online proceedings see the URL:
http://www.cs.ubc.ca/spider/poole/UAI95.html
The program for UAI-95 is as follows:
UAI-95 - 11th Conference on Uncertainty in AI
McGill University, Montreal, Quebec, 18-20 August 1995
===================================
Final Program
===================================
==============================
Friday 18 August Overview
==============================
08:45 -- 09:00 Opening remarks
09:00 -- 10:15 Invited talk #1 (Haussler)
10:15 -- 10:30 Break
10:30 -- 12:30 Presentation session #1
12:30 -- 14:00 Lunch
14:00 -- 16:00 Poster session #1
16:00 -- 16:15 Break
16:30 -- 18:30 Presentation session #2
================================
Saturday 19 August Overview
================================
09:00 -- 10:30 Invited talk #2 (Jordan) + panel discussion
10:30 -- 10:45 Break
10:45 -- 12:45 Presentation session #3
12:45 -- 14:30 Lunch
14:30 -- 16:00 Invited talk #3 (Subrahmanian)
16:00 -- 16:15 Break
16:15 -- 18:15 Presentation session #4
================================
Sunday 20 August Overview
================================
09:00 -- 10:30 Invited talk #4 (Shafer) + panel discussion
10:30 -- 10:45 Break
10:45 -- 12:45 Presentation session #5
12:45 -- 14:30 Lunch
14:30 -- 16:00 Poster session #2
16:00 -- 16:15 Break
16:15 -- 18:15 Presentation session #6
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Invited talks
==============================================
#1 Haussler
"Hidden Markov and Related Statistical Models:
How They Have Been Applied to Biosequence Analysis"
#2 Jordan (with panel on learning)
"A Few Relevant Ideas from Statistics, Neural Networks,
and Statistical Mechanics"
#3 Subrahamanian
"Uncertainty in Deductive Databases"
#4 Shafer (with panel on causality)
"The Multiple Causal Interpretation of Bayes Nets"
=================================================
Presentation session #1
=================================================
Wellman/Ford/Larson
PATH PLANNING UNDER TIME-DEPENDENT UNCERTAINTY
Horvitz/Barry
DISPLAY OF INFORMATION FOR TIME-CRITICAL DECISION MAKING
Pearl/Robins
PROBABILISTIC EVALUATION OF SEQUENTIAL PLANS FROM CAUSAL MODELS WITH HIDDEN VARIABLES
Haddawy/Doan/Goodwin
EFFICIENT DECISION-THEORETIC PLANNING: TECHNIQUES AND EMPIRICAL ANALYSIS
Fargier/Lang/Clouaire/Schiex
A CONSTRAINT SATISFACTION FRAMEWORK FOR DECISION UNDER UNCERTAINTY
=================================================
Presentation session #2
=================================================
Xu/Smets
GENERATING EXPLANATIONS FOR EVIDENTIAL REASONING
========> Best student paper <===========
Meek
CAUSAL INFERENCE AND CAUSAL EXPLANATION WITH BACKGROUND KNOWLEDGE
========> Best student paper <===========
Cayrac/Dubois/Prade
PRACTICAL MODEL-BASED DIAGNOSIS WITH QUALITATIVE POSSIBILISTIC UNCERTAINTY
Srinivas/Horvitz
EXPLOITING SYSTEM HIERARCHY TO COMPUTE REPAIR PLANS IN PROBABILISTIC MODEL-BASED DIAGNOSIS
Balke/Pearl
COUNTERFACTUALS AND POLICY ANALYSIS IN STRUCTURAL MODELS
=================================================
Presentation session #3
=================================================
Jensen
CAUTIOUS PROPAGATION IN BAYESIAN NETWORKS
Darwiche
STRONG CONDITIONING ALGORITHMS FOR EXACT AND APPROXIMATE INFERENCE IN CAUSAL NETWORKS
========> Best student paper <===========
Draper
CLUSTERING WITHOUT (THINKING ABOUT) TRIANGULATION
========> Best student paper <===========
Goldszmidt
FAST BELIEF UPDATE USING ORDER-OF-MAGNITUDE PROBABILITIES
========> Best student paper <===========
Harmanec
TOWARD A CHARACTERIZATION OF UNCERTAINTY MEASURE FOR THE DEMPSTER-SHAFER THEORY
========> Best student paper <===========
=================================================
Presentation session #4
=================================================
Dubois/Prade
NUMERICAL REPRESENTATION OF ACCEPTANCE
Grosof
TRANSFORMING PRIORITIZED DEFAULTS AND SPECIFICITY INTO PARALLEL DEFAULTS
Weydert
DEFAULTS AND INFINITESIMALS DEFEASIBLE INFERENCE BY NONARCHIMEDEAN ENTROPY-MAXIMIZATION
Benferhat/Saffiotti/Smets
BELIEF FUNCTIONS AND DEFAULT REASONING
Ngo/Haddawy/Helwig
A THEORETICAL FRAMEWORK FOR CONTEXT-SENSITIVE TEMPORAL PROBABILITY MODEL
CONSTRUCTION WITH APPLICATION TO PLAN PROJECTION
==========================================
Presentation session #5
==========================================
Campos/Moral
INDEPENDENCE CONCEPTS FOR CONVEX SETS OF PROBABILITIES
Geiger/Heckerman
A CHARACTERIZATION OF THE DIRICHLET DISTRIBUTION THROUGH GLOBAL AND LOCAL INDEPENDENCE
Spirtes
DIRECTED CYCLIC GRAPHICAL REPRESENTATIONS OF FEEDBACK MODELS
Pynadath/Wellman
ACCOUNTING FOR CONTEXT IN PLAN RECOGNITION, WITH APPLICATION TO TRAFFIC MONITORING
Srinivas
MODELING FAILURE PRIORS AND PERSISTENCE IN MODEL-BASED DIAGNOSIS
==========================================
Presentation session #6
==========================================
Poole
EXPLOITING THE RULE STRUCTURE FOR DECISION MAKING WITHIN THE INDEPENDENT CHOICE LOGIC
Krause/Fox/Judson
IS THERE A ROLE FOR QUALITATIVE RISK ASSESSMENT?
Srinivas
POLYNOMIAL ALGORITHM FOR COMPUTING THE OPTIMAL REPAIR STRATEGY IN A SYSTEM
WITH INDEPENDENT COMPONENT FAILURES
Boldrin/Sossai
AN ALGEBRAIC SEMANTICS FOR POSSIBILISTIC LOGIC
Hajek/Godo/Esteva
FUZZY LOGIC AND PROBABILITY
==============================================================
Poster session #1
==============================================================
1. Jack Breese, Russ Blake.
AUTOMATING COMPUTER BOTTLENECK DETECTION WITH BELIEF NETS
2. Wray L. Buntine
CHAIN GRAPHS FOR LEARNING
3. J.L. Castro, J.M. Zurita
AN APPROACH TO GET THE STRUCTURE OF A FUZZY RULE UNDER UNCERTAINTY
4. Tom Chavez, Ross Shachter
DECISION FLEXIBILITY
5. Arthur L. Delcher, Adam Grove, Simon Kasif, Judea Pearl
LOGARITHMIC-TIME UPDATES AND QUERIES IN PROBABILISTIC NETWORKS
6. Eric Driver, Darryl Morrell
CONTINUOUS BAYESIAN NETWORKS
7. Nir Friedman, Joseph Y. Halpern
PLAUSIBILITY MEASURES: A USER'S GUIDE
8. David Galles, Judea Pearl
TESTING IDENTIFIABILITY OF CAUSAL EFFECTS
9. Steve Hanks, David Madigan, Jonathan Gavrin
PROBABILISTIC TEMPORAL REASONING WITH ENDOGENOUS CHANGE
10. David Heckerman
BAYESIAN METHODS FOR LEARNING CAUSAL NETWORKS
11. Eric Horvitz, Adrian Klein
STUDIES IN FLEXIBLE LOGICAL INFERENCE: A DECISION-MAKING PERSPECTIVE
12. George John, Pat Langley
ESTIMATING CONTINUOUS DISTRIBUTIONS IN BAYESIAN CLASSIFIERS
13. Uffe Kjaerulff
HUGS: COMBINING EXACT INFERENCE AND GIBBS SAMPLING IN JUNCTION TREES
14. Prakash P. Shenoy
A NEW PRUNING METHOD FOR SOLVING DECISION TREES AND GAME TREES
15. Peter Spirtes, Christopher Meek, Thomas Richardson
CAUSAL INFERENCE IN THE PRESENCE OF LATENT VARIABLES AND SELECTION BIAS
16. Nic Wilson
AN ORDER OF MAGNITUDE CALCULUS
17. S.K.M. Wong, C.J. Butz, Y. Xiang
A METHOD FOR IMPLEMENTING A PROBABILISTIC MODEL AS A RELATIONAL DATABASE<br>
18. Y. Xiang
OPTIMIZATION OF INTER-SUBNET BELIEF UPDATING IN MULTIPLY SECTIONED BAYESIAN NETWORKS
19. Nevin Lianwen Zhang
INFERENCE WITH CAUSAL INDEPENDENCE IN THE CPSC NETWORK
===============================================
Poster Session #2
===============================================
1. Fahiem Bacchus, Adam Grove
GRAPHICAL MODELS FOR PREFERENCE AND UTILITY
2. Enrique Castillo, Remco R. Bouckaert, Jose Maria Sarabia,
ERROR ESTIMATION IN APPROXIMATE BAYESIAN BELIEF NETWORK INFERENCE
3. David Maxwell Chickering
A NEW CHARACTERIZATION OF EQUIVALENT BAYESIAN NETWORK STRUCTURES
4. Marek J. Druzdzel, Linda C. van der Gaag
ELICITATION OF PROBABILITIES: COMBINING QUALITATIVE AND QUANTITATIVE INFORMATION
5. Kazuo J. Ezawa, Til Schuermann
LEARNING SYSTEM: A RARE BINARY OUTCOME WITH MIXED DATA STRUCTURES
6. David Heckerman, Dan Geiger
LEARNING BAYESIAN NETWORKS: A UNIFICATION FOR DISCRETE AND GAUSSIAN DOMAINS
7. David Heckerman, Ross Shachter
A DEFINITION AND GRAPHICAL REPRESENTATION FOR CAUSALITY
8. Mark Hulme
IMPROVED SAMPLING FOR DIAGNOSTIC REASONING IN BAYESIAN NETWORK
9. Ali Jenzarli
INFORMATION/RELEVANCE INFLUENCE DIAGRAMS
10. Keiji Kanazawa, Daphne Koller, Stuart Russell
STOCHASTIC SIMULATION ALGORITHMS FOR DYNAMIC PROBABILISTIC NETWORKS
11. Grigoris I. Karakoulas
PROBABILISTIC EXPLORATION IN PLANNING WHILE LEARNING
12. Alexander V. Kozlov, Jaswinder Pal Singh
APPROXIMATE PROBABILISTIC INFERENCE IN BELIEF NETWORKS
13. Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling
ON THE COMPLEXITY OF SOLVING MARKOV DECISION PROBLEMS
14. Chris Meek
STRONG-COMPLETENESS AND FAITHFULNESS IN BAYES NETWORKS
15. Simon Parsons
REFINING REASONING IN QUALITATIVE PROBABILISTIC NETWORKS
16. Judea Pearl
ON THE TESTABILITY OF CAUSAL MODELS WITH LATENT AND INSTRUMENTAL VARIABLES
17. Gregory Provan
ABSTRACTION IN BELIEF NETWORKS: THE ROLE OF INTERMEDIATE STATES IN DIAGNOSTIC REASONING
18. Marco Valtorta, Young-Gyun Kim
ON THE DETECTION OF CONFLICTS IN DIAGNOSTIC BAYESIAN NETWORKS USING ABSTRACTION
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David Poole, Office: +1 (604) 822-6254
Department of Computer Science, Fax: +1 (604) 822-5485
University of British Columbia, Email: poole at cs.ubc.ca
2366 Main Mall, URL: http://www.cs.ubc.ca/spider/poole
Vancouver, B.C., Canada V6T 1Z4 FTP: ftp://ftp.cs.ubc.ca/ftp/local/poole
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