UAI '97 program and registration information
Eric Horvitz
horvitz at MICROSOFT.com
Tue Jun 6 06:52:25 EDT 2006
Dear Colleague:
I have appended program and registration information for the Thirteenth
Conference on Uncertainty and Artificial Intelligence (UAI '97). More
details and an online registration form are linked to the UAI '97 home
page at http://cuai97.microsoft.com. UAI '97 will be held at Brown
University in Providence, Rhode Island, August 1-3. In addition to the
main program, you may find interesting the Full-Day Course on Uncertain
Reasoning which will be held on Thursday, July 31. Details on the
course can be found at http://cuai97.microsoft.com/course.htm. Please
register for the conference and/or the course before early registration
comes to an end on May 31, 1997.
I would be happy to answer any additional questions about the
conference.
Best regards,
Eric Horvitz
Conference Chair
====================================================
Thirteenth Conference on Uncertainty in Artificial Intelligence
(UAI '97)
http://cuai97.microsoft.com
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
http://cuai97.microsoft.com/register/reg.htm
Full-Day Course on Uncertain Reasoning 8:30-6:00pm
http://cuai97.microsoft.com/course.htm
_____________________________________________
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
Daphne Koller and Avi Pfeffer
(winner of the best student paper award)
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 and David Heckerman
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
Model Selection for Bayesian-Network Classifiers
David Heckerman and Christopher Meek
Time-Critical Action
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.
Estimation of Effects of Sequential Treatments By Reparameterizing
Directed Acyclic Graphs
James M. Robins and Larry Wasserman
** 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
__________________________________________________________
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
A Decision-Theoretic Approach to Graphics Rendering
Eric Horvitz and Jed Lengyel
* Break 10:40-11:00am
* Panel Discussion: 11:00-12:00am
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 3:00-3:30am
** 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 about the UAI '97 program, contact the UAI '97
Program Chairs, Dan Geiger and Prakash P. Shenoy. For other questions
about UAI '97, please contact the Conference Chair, Eric Horvitz.
* * *
UAI '97 Conference Chair
Eric Horvitz (horvitz at microsoft.com)
Microsoft Research, 9S
Redmond, WA, USA
http://research.microsoft.com/~horvitz
UAI '97 Program Chairs
Dan Geiger (dang at cs.technion.ac.il)
Computer Science Department
Technion, Israel Institute of Technology
Prakash Shenoy (pshenoy at ukans.edu)
School of Business
University of Kansas
http://pshenoy@stat1.cc.ukans.edu/~pshenoy/
====================================================
To register for UAI '97, please use the online registration form at:
http://cuai97.microsoft.com/register/reg.htm
If you do not have access to the web, please use the appended ascii
form.
Detailed information on accomodations can be found at
http://cuai97.microsoft.com/#lodge. Several blocks of rooms of on-campus
housing at Brown University have been reserved for UAI attendees on a
first come, first serve basis. In addition, there are five hotels within
a 1 mile radius from the UAI Conference (see
http://www.providenceri.com/as220/hotels.html for additional information
on hotels).
Travel information is available at:
http://cuai97.microsoft.com/#trav
======================================================
***** UAI '97 Registration Form *****
(If possible, please use the online form at
http://cuai97.microsoft.com/register/reg.htm)
------------------------------------------------------------------------
-----------------
* Name (Last, First): _____________________________
* Affiliation: ___________________________
* Email address: ___________________________
* Mailing address: ___________________________
* Telephone: ___________________________
------------------------------------------------------------------------
-----------------
** Registration Fees:
>>> Main Conference <<<<
Fees (please circle and tally below):
Early Registration: $225, Late Registration (After May 31): $285
Student Registration (certify below): $125, Late Registration (After
May 31): $150
* * *
>>> Full-Day Course on Uncertain Reasoning (July 31, 1997) <<<
* Fees:
With Conference Registration: $75, Without Conference: $125
Student (certify below):
With Conference Registration: $35, Without Conference: $55
The registration fee includes the conference banquet on August 2nd and a
package of three lunches which will be served on campus.
* Student certification
I am a full-time student at the following
institution:____________________
Academic advisor's name:____________________
Academic advisor's email:____________________
* Conference Registration Fees: U.S. $ ________________________
Full-Day Course: U.S. $ ________________________
TOTAL: U.S. $ ________________________
______________________________________________________
Please make check payable to: AUAI or Association for Uncertainty in
Artificial Intelligence
Or
Indicate credit card payment(s) enclosed:
______ Mastercard ______ Visa
Credit Card No.: _____________________________________________
Exp. Date:
________________________
Signature: ____________________________
For credit card payment, you may fax this form to: (206) 936-1616
Registrations by check/money order should be mailed to:
Eric Horvitz
Microsoft Research, 9S
Redmond, WA 98052-6399
Fax: 206-936-1616
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