Thirteenth Conference on Uncertainty in Artificial Intelligence
August 1-3, 1997
Brown University
Providence, Rhode Island, USA
UAI '97 Conference Program
Thursday, July 31, 1997
Conference and Course Registration 8:00-8:30am
Friday, August 1, 1997
Main Conference Registration 8:00-8:25am
Opening Remarks
Dan Geiger and Prakash P. Shenoy
8:25-8:30am
Invited talk I: Local Computation Algorithms
Steffen L. Lauritzen
8:30-9:30am
Abstract:
Inference in probabilistic expert systems has been made possible through
the development of efficient algorithms that in one way or another
involve
message passing between local entities arranged to form a junction tree.
Many of these algorithms have a common structure which can be partly
formalized in abstract axioms with an algebraic flavor. However, the
existing abstract frameworks do not fully capture all interesting cases
of such local computation algorithms.
The lecture will describe the basic elements of the algorithms, give
examples of interesting local computations that are covered by current
abstract frameworks, and also examples of interesting computations that
are not, with a view towards reaching a fuller exploitation of the
potential in these ideas.
Invited talk II: Coding Theory and Probability Propagation
in Loopy Bayesian Networks
Robert J. McEliece
9:30-10:30am
Abstract:
In 1993 a group coding researchers in France devised, as part of their
astonishing "turbo code" breakthrough, a remarkable iterative
decoding algorithm. This algorithm can be viewed as an inference
algorithm on a Bayesian network, but (a) it is approximate, not exact,
and (b) it violates a sacred assumption in Bayesian analysis, viz.,
that the network should have no loops. Indeed, it is accurate to say
that the turbo decoding algorithm is functionally equivalent to Pearl's
algorithm applied to a certain directed bipartite graph in which the
messages circulate around indefinitely, until either convergence is
reached, or (more realistically) for a fixed number of cycles. With
hindsight, it is possible to trace a continuous chain of "loopy" belief
propagation algorithms within the coding community beginning in 1962
(with Gallager's iterative decoding algorithm for low density parity
check codes), continued in 1981 by Tanner and much more recently
(1995-1996) by Wiberg and MacKay-Neal.
In this talk I'd like to challenge the UAI community to reassess the
conventional wisdom that probability propagation only works in trees,
since the coding community has now accumulated considerable
experimental evidence that in some cases at least, "loopy" belief
propagation works, at least approximately.
Along the way, I'll do my best to bring the AI audience up to speed on
the latest developments in coding. My emphasis will be on
convolutional codes, since they are the building blocks for
turbo-codes. I will mention that two of the most important (pre-turbo)
decoding algorithms, viz. Viterbi (1967) and BCJR (1974) can be stated
in orthodox Bayesian network terms. BCJR, for example, is an
anticipation of Pearls' algorithm on a special kind of tree, and
Viterbi's algorithm gives a solution to the "most probable
explanation" problem on the same structure. Thus coding theorists and
AI people have been working on, and solving, similar problems for a
long time. It would be nice if they became more aware of each other's
work.
Break 10:30-11:00am
Plenary Session I: Modeling
11:00-12:00am
-
Object-Oriented Bayesian Networks
(winner of the best student paper award)
Daphne Koller and Avi Pfeffer
-
Problem-Focused Incremental Elicitation of
Multi-Attribute Utility Models
Vu Ha and Peter Haddawy
-
Representing Aggregate Belief through the
Competitive Equilibrium of a Securities Market
David M. Pennock and Michael P. Wellman
Lunch 12:00-1:30pm
Plenary Session II: Learning & Clustering
1:30-3:00pm
-
A Bayesian Approach to Learning Bayesian Networks
with Local Structure
David Maxwell Chickering, David Heckerman, and Chris Meek
-
Batch and On-line Parameter Estimation in
Bayesian Networks
Eric Bauer, Daphne Koller, and Yoram Singer
-
Sequential Update of Bayesian Networks Structure
Nir Friedman and Moises Goldszmidt
-
An Information-Theoretic Analysis of
Hard and Soft Assignment Methods for Clustering
Michael Kearns, Yishay Mansour, and Andrew Ng
Poster Session I: Overview Presentations
3:00-3:30pm
Poster Session I
3:30-5:30pm
-
Algorithms for Learning Decomposable Models and
Chordal Graphs
Luis M. de Campos and Juan F. Huete
-
Defining Explanation in Probabilistic Systems
Urszula Chajewska and Joseph Y. Halpern
-
Exploring Parallelism in Learning Belief
Networks
T. Chu and Yang Xiang
-
Efficient Induction of Finite State Automata
Matthew S. Collins and Jonathon J. Oliver
-
A Scheme for Approximating Probabilistic
Inference
Rina Dechter and Irina Rish
-
Limitations of Skeptical Default Reasoning
Jens Doerpmund
-
The Complexity of Plan Existence and Evaluation
in Probabilistic Domains
Judy Goldsmith, Michael L. Littman, and Martin
Mundhenk
-
Learning Bayesian Nets that Perform Well
Russell Greiner, Dale Schuurmans, and Adam Grove
-
Model Selection for Bayesian-Network Classifiers
David Heckerman and Christopher Meek
-
Time-Critical Action: Representations and Application
Eric Horvitz and Adam Seiver
-
Composition of Probability Measures on Finite
Spaces
Radim Jirousek
-
Computational Advantages of Relevance Reasoning
in Bayesian Belief Networks
Yan Lin and Marek J. Druzdzel
-
Support and Plausibility Degrees in
Generalized Functional Models
Paul-Andre Monney
-
On Stable Multi-Agent Behavior in Face of
Uncertainty
Moshe Tennenholtz
-
Cost-Sharing in Bayesian Knowledge Bases
Solomon Eyal Shimony, Carmel Domshlak and Eugene
Santos Jr.
-
Independence of Causal Influence and Clique Tree
Propagation
Nevin L. Zhang and Li Yan
Saturday, August 2, 1997
Invited talk III: Genetic Linkage Analysis
Alejandro A. Schaffer
8:30-9:30am
Abstract:
Genetic linkage analysis is a collection of statistical techniques used
to infer the approximate chromosomal location of disease susceptibility
genes using family tree data. Among the widely publicized linkage
discoveries
in 1996 were the approximate locations of genes conferring
susceptibility
to Parkinson's disease, prostate cancer, Crohn's disease, and
adult-onset
diabetes. Most linkage analysis methods are based on maximum likelihood
estimation. Parametric linkage analysis methods use probabilistic
inference on Bayesian networks, which is also used in the UAI community.
I will give a self-contained overview of the genetics, statistics,
algorithms, and software used in real linkage analysis studies.
Plenary Session III: Markov Decision Processes
9:30-10:30am
-
Model Reduction Techniques for Computing
Approximately Optimal
Solutions for Markov Decision Processes
Thomas Dean, Robert Givan and Sonia Leach
-
Incremental Pruning: A Simple, Fast, Exact
Algorithm for Partially Observable Markov Decision
Processes
Anthony Cassandra, Michael L. Littman and Nevin
L. Zhang
-
Region-based Approximations for Planing in
Stochastic Domains
Nevin L. Zhang and Wenju Liu
Break 10:30-11:00am
Panel Discussion: 11:00-12:00am
Lunch 12:00-1:30pm
Plenary Session IV: Foundations
1:30-3:00pm
-
Two Senses of Utility Independence
Yoav Shoham
-
Probability Update: Conditioning vs.
Cross-Entropy
Adam J. Grove and Joseph Y. Halpern
-
Probabilistic Acceptance
Henry E. Kyburg Jr.
Poster Session II: Overview Presentations
3:00-3:30pm
Poster Session II
3:30-5:30pm
-
Network Fragments: Representing Knowledge for
Probabilistic Models
Kathryn Blackmond Laskey and Suzanne M. Mahoney
-
Correlated Action Effects in Decision Theoretic
Regression
Craig Boutilier
-
A Standard Approach for Optimizing
Belief-Network Inference
Adnan Darwiche and Gregory Provan
-
Myopic Value of Information for Influence
Diagrams
Soren L. Dittmer and Finn V. Jensen
-
Algorithm Portfolio Design Theory vs. Practice
Carla P. Gomes and Bart Selman
-
Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels
J. Hu and Yang Xiang
-
Relational Bayesian Networks
Manfred Jaeger
-
A Target Classification Decision Aid
Todd Michael Mansell
-
Structure and Parameter Learning for Causal
Independence and Causal Interactions Models
Christopher Meek and David Heckerman
-
An Investigation into the Cognitive Processing of
Causal Knowledge
Richard E. Neapolitan, Scott B. Morris,
and Doug Cork
-
Learning Bayesian Networks from Incomplete
Databases
Marco Ramoni and Paola Sebastiani
-
Incremental Map Generation by Low Cost Robots
Based on Possibility/Necessity Grids
M. Lopez Sanchez, R. Lopez de Mantaras,
and C. Sierra
-
Sequential Thresholds: Evolving Context of
Default Extensions
Choh Man Teng
-
Score and Information for Recursive Exponential
Models with Incomplete Data
Bo Thiesson
-
Fast Value Iteration for Goal-Directed
Markov Decision Processes
Nevin L. Zhang and Weihong Zhang
Saturday Evening, August 2, 1997
UAI '97 Dinner Banquet
Banquet Talk:
How I Became Uncertain, Eugene Charniak
Sunday, August 3, 1997
Invited talk IV:
Gaussian processes - a replacement for supervised neural networks?
David J.C. MacKay
8:20-9:20am
Abstract:
Feedforward neural networks such as multilayer perceptrons are popular
tools for nonlinear regression and classification problems. From a
Bayesian perspective, a choice of a neural network model can be viewed
as defining a prior probability distribution over non-linear
functions, and the neural network's learning process can be
interpreted in terms of the posterior probability distribution over
the unknown function. (Some learning algorithms search for the
function with maximum posterior probability and other Monte Carlo
methods draw samples from this posterior probability).
In the limit of large but otherwise standard networks, Neal (1996) has
shown that the prior distribution over non-linear functions implied by
the Bayesian neural network falls in a class of probability
distributions known as Gaussian processes. The hyperparameters of the
neural network model determine the characteristic lengthscales of the
Gaussian process. Neal's observation motivates the idea of discarding
parameterized networks and working directly with Gaussian
processes. Computations in which the parameters of the network are
optimized are then replaced by simple matrix operations using the
covariance matrix of the Gaussian process.
In this talk I will review work on this idea by Neal, Williams,
Rasmussen, Barber, Gibbs and MacKay, and will assess whether, for
supervised regression and classification tasks, the feedforward
network has been superceded.
Plenary Session V: Applications of Uncertain Reasoning
9:20-10:40am
-
Bayes Networks for Sonar Sensor Fusion
Ami Berler and Solomon Eyal Shimony
-
Image Segmentation in Video Sequences: A
Probabilistic Approach
Nir Friedman and Stuart Russell
-
Lexical Access for Speech Understanding using
Minimum Message Length Encoding
Ian Thomas, Ingrid Zukerman, Bhavani Raskutti,
Jonathan Oliver, David Albrecht
-
Perception, Attention, and Resources: A Decision-Theoretic Approach to Graphics Rendering
Eric Horvitz and Jed Lengyel
Break 10:40-11:00am
Panel Discussion: 11:00-12:00pm
Lunch 12:00-1:30pm
Plenary Session VI: Developments in Belief and Possibility
1:30-3:00pm
-
Decision-making under Ordinal Preferences
and Comparative Uncertainty
D. Dubois, H. Fargier, and H. Prade
-
Inference with Idempotent Valuations
Luis D. Hernandez and Serafin Moral
-
Corporate Evidential Decision Making in
Performance Prediction Domains
A.G. Buchner, W. Dubitzky, A. Schuster, P. Lopes
P.G. O'Donoghue, J.G. Hughes, D.A. Bell, K.
Adamson, J.A. White, J. Anderson, M.D. Mulvenna
-
Exploiting Uncertain and Temporal Information in
Correlation
John Bigham
Break 3:00-3:30pm
Plenary Session VII: Topics on Inference
3:30-5:00pm
-
Nonuniform Dynamic Discretization in Hybrid
Networks
Alexander V. Kozlov and Daphne Koller
-
Robustness Analysis of Bayesian Networks with
Local Convex Sets of Distributions
Fabio Cozman
-
Structured Arc Reversal and Simulation of
Dynamic Probabilistic Networks
Adrian Y. W. Cheuk and Craig Boutilier
-
Nested Junction Trees
Uffe Kjaerulff
If you have questions or comments about the UAI '97 program, contact
the UAI '97 Program Chairs: Dan
Geiger and Prakash P.
Shenoy. For questions about the UAI '97 conference, please contact the Conference Chair, Eric
Horvitz.