WORKSHOP: Uncertainty99

JOE WHITTAKER (joe.whittaker@lancaster.ac.uk)
Tue, 24 Mar 1998 00:11:04 +0100 (MET)

Call for Papers: UNCERTAINTY 99
Seventh International Workshop on Artificial Intelligence and Statistics
January 3-6, 1999,
Ft. Lauderdale, Florida
http://uncertainty99.microsoft.com/

This is the seventh in a series of workshops which has brought
together researchers in Artificial Intelligence (AI) and in Statistics
to discuss problems of mutual interest. The exchange has broadened
research in both fields and has strongly encouraged interdisciplinary
work. Papers on all aspects of the interface between AI & Statistics
are encouraged.

To encourage interaction and a broad exchange of ideas, the
presentations will be limited to about 20 discussion papers in single
session meetings over three days (Jan. 4-6). Focused poster sessions
will provide the means for presenting and discussing the remaining
research papers. Papers for poster sessions will be treated equally
with papers for presentation in publications. Attendance at the
workshop will not be limited.

The three days of research presentations will be preceded by a
day of tutorials (Jan. 3). These are intended to expose researchers in
each field to the methodology used in the other field. The tutorial
speakers will include

Chris Bishop, Cambridge,
Latent variables and neural networks.
Sue Dumais, Seattle,
Information access and retrieval.

and the keynote speaker is

David Spiegelhalter, Cambridge, on
Bayesian statistical analysis.

Topics of Interest:

Statistics in AI:
vision, robotics, natural language processing, speech recognition
AI in statistics:
statistical advisory systems, experimental design
Automated data analysis
Cluster analysis and unsupervised learning
Integrated man-machine modeling methods
Interpretability in modelling
Knowledge discovery in databases
Learning
Metadata and the design of statistical data bases
Model uncertainty, multiple models
Multivariate graphical models, belief networks, causal modeling
Online analytic processing in statistics
Pattern recognition
Predictive modelling: classification and regression
Probabilistic neural networks
Probability and search
Statistical strategy
Visualization of very large datasets

This list is not intended to define an exclusive list of topics of
interest. Authors are encouraged to submit papers on any topic which
falls within the intersection of AI and Statistics.

Submission Requirements:

An extended abstract (up to 4 pages) should be emailed
(either ascii, word, postscript or a WWW address) to
joe.whittaker@lancaster.ac.uk
Telephone: +44 (0)1524 593960

or, as a last resort, four paper copies should be mailed to

Joe Whittaker Program Chair
7th International Workshop on AI and Statistics
Department of Mathematics and Statistics
Lancaster University, Lancaster, LA1 4YF, England

Submissions will be considered if they are received by midnight July
1, 1998. Please indicate which topic(s) your abstract addresses. Receipt
of all submissions will be confirmed via electronic mail. Acceptance
notices will be emailed by September 1, 1998.

Preliminary papers (up to 20 pages) must be received by November 1,
1998. These preliminary papers will be copied and distributed at the
workshop.


Program Chairs:
David Heckerman, Microsoft, heckerma@microsoft.com
Joe Whittaker, Lancaster University, joe.whittaker@lancaster.ac.uk

Program Committee:

Russell Almond, ETS, Princeton, ralmond@ets.org
Chris Bishop, Microsoft Research, Cambridge, cmbishop@microsoft.com
Wray Buntine, Thinkbank, Inc., wray@ultimode.com
Peter Cheeseman, NASA Ames, cheeseman@kronos.arc.nasa.gov
Max Chickering, Microsoft, dmax@microsoft.com
Paul Cohen, University of Massachusetts, cohen@cs.umass.edu
Greg Cooper, University of Pittsburgh, gfc@smi.med.pitt.edu
Phil Dawid, UC, London, dawid@stats.ucl.ac.uk
David Dowe, Monash University, David.Dowe@fcit.monash.edu.au
William DuMouchel, AT&T, dumouchel@research.att.com
Sue Dumais, Microsoft Seattle, sdumais@microsoft.com
David Edwards, Novo, DED@novo.dk
Doug Fisher, Vanderbilt University, dfisher@vuse.vanderbilt.edu
Nir Friedman, Berkeley, nir@cs.berkeley.edu
Dan Geiger, Technion, dang@cs.technion.ac.il
Edward George, University of Texas, egeorge@mail.utexas.edu
Clark Glymour, Carnegie-Mellon University, cg09@andrew.cmu.edu
David Hand, Open University, d.j.hand@open.ac.uk
Geoff Hinton, University of Toronto, hinton@ai.toronto.edu
Tommi Jaakkola, MIT, tommi@life.ai.mit.edu
Michael Jordan, Univ. California Berkeley, jordan@cs.berkeley.edu
Michael Kearns, AT & T , mkearns@research.att.com
Daphne Koller, koller@fiery.stanford.edu
Steffen Lauritzen, Aalborg University, steffen@math.auc.dk
Hans Lenz, Free University of Berlin, hjlenz@wiwiss.fu-berlin.de
David Lewis, AT&T Labs, lewis@research.att.com
David Madigan, University of Washington, madigan@stat.washington.edu
Andrew Moore, Carnegie-Mellon University, awm@cs.cmu.edu
Daryl Pregibon, AT&T Labs, daryl@research.att.com
Thomas Richardson, Univ. Wash, tsr@stat.washington.edu,
Alberto Roverato, Universita di Modena, roverato@unimo.it
Lawrence Saul, AT&T Labs, lsaul@research.att.com
Richard Scheines, Carnegie-Mellon University, rs2l+@andrew.cmu.edu
Sebastian Seung, Bell Labs, Lucent Technologies, seung@physics.lucent.com
Prakash Shenoy, University of Kansas, pshenoy@ukans.edu
Padhraic Smyth, JPL and UCI, smyth@sifnos.ics.uci.edu
David Spiegelhalter, MRC, Cambridge, david.spiegelhalter@mrc-bsu.cam.ac.uk
Peter Spirtes, Carnegie-Mellon University, ps7z@andrew.cmu.edu
Milan Studeny, Praha, studeny@utia.cas.cz
Nanny Wermuth, Mainz University, wermuth@animal.sowi.uni-mainz.de