[Artificial Intelligence and Statistics Logo] Artificial Intelligence and Statistics 1997

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Saturday Jan 4

8:30-11:30 Tutorial A

Conditional Independence for Statistics and AI
A. P. Dawid, University College London

12:30-3:30 Tutorial B

Bayesian Time Series Analysis and Forecasting
Mike West, Duke University

12:30-3:30 Tutorial C

Learning in Information Agents
Tom M. Mitchell, Carnegie Mellon University

4:00-7:00 Tutorial D

Graphical models, neural networks and machine learning algorithms
Michael Jordan, MIT

Sunday Jan 5

7:30 to 8:45 CONTINENTAL BREAKFAST/REGISTRATION

8:45 to 9:00 OPENING COMMENTS

9:00 to 10:30 SESSION 1:

A Bayesian approach to CART
Hugh Chipman, Edward I George, & Robert E. McCulloch

A comparison of scientific and engineering criteria for Bayesian model selection
David Heckerman & David Maxwell Chickering

Strategies for model mixing in generalized linear models
Merlise Clyde

10:30 to 11:00 COFFEE BREAK

11:00 to 12:00 SESSION 2:

Variational inference for continuous sigmoidal belief networks
Brendan J. Frey

Extensions of undirected and acyclic, directed graphical models
Thomas Richardson

12:00 to 1:00 LUNCH (provided)

1:00 to 4:00 BREAK

4:00 to 6:00 POSTER SUMMARIES

6:00 to 7:00 DINNER

7:00 to 9:30 POSTER SESSIONS


Monday Jan 6

8:00 to 9:00 CONTINENTAL BREAKFAST

9:00 to 10:30 SESSION 3:

A note on cyclic graphs and dynamical feedback systems
Thomas Richardson, Peter Spirtes, & Clark Glymour

Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation
Richard Scheines

Using classification trees to improve causal inferences in observational studies
Louis Anthony Cox

10:30 to 11:00 COFFEE BREAK

11:00 to 12:30 SESSION 4:

Building an EDA Assistant: A Progress Report
Robert St. Amant & Paul R. Cohen

Mixed memory Markov models
Lawrence K. Saul & Michael I Jordan

Wavelet based random densities
David Rios Insua & Brani Vidakovic

12:30 to 2:00 LUNCH (provided)

2:00 to 3:30 SESSION 5:

Using Prediction to Improve Combinatorial Optimization Search
Justin A. Boyan & Andrew W. Moore

Inference using Probabilistic Concept Trees
Doug Fisher & Doug Talbert

The Effects of Training Set Size on Decision Tree Complexity
Tim Oates & David Jensen

3:30 to 4:30 BREAK

4:00 to 5:00 BUSINESS MEETING


Tuesday Jan 7

8:30 to 9:00 CONTINENTAL BREAKFAST

9:00 to 10:30 SESSION 6:

WWW Cache Layout to Ease Network Overload
Kenichi Yoshida

PAC learning with constant-partition classification noise and applications to decision tree induction
Scott E. Decatur

Graphical Model Based Computer Adaptive Testing
Russell G. Almond & Robert J. Mislevy

10:30 to 11:00 COFFEE BREAK

11:00 to 12:00 SESSION 7:

Asessing and Improving Classification Rules
David J. Hand, Keming Yu, & Niall Ada

A variational approach to Bayesian logistic regression models and their extensions
Tommi S. Jaakkola & Michael I. Jordan

12:00 to 12:15 CLOSING COMMENTS

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