CRM/
MITACS/
IRIS
Workshop on Selecting and Combining Models with Machine Learning Algorithms
Montreal, Québec, Canada,
April 11-14, 2000
Organizers: Yoshua Bengio
(Université de Montréal) and
Dale
Schuurmans (University of Waterloo)
Location of the conferences:
Room 1140 (1st floor), Pavillon André-Aisenstadt,
Université de Montréal, 2920 Chemin de la Tour, Montréal
Workshop Objectives
A central objective of machine learning research is to develop algorithms
that learn predictive relationships from data. This is a central component
of data mining and knowledge discovery tasks, which are becoming commonplace
applications in the realm of e-commerce. This is a difficult task, however,
because inferring a predictive function from data is in fact an "ill-posed"
problem; that is, many functions can often "fit" a given finite data set,
and yet these functions might generalize very differently on new data drawn
from the same distribution.
To make this problem well-posed one needs to somehow "calibrate" the
complexity of the proposed function class to the amount and quality of
available sample data. A classical approach is to perform "model selection"
where one imposes a preference structure over function classes and then
optimizes a combined objective of class preference and data fit. In doing
so, however, it would be useful to have an accurate estimate of the expected
generalization performance at each preference level; one could then pick
the function class that obtained the lowest expected error, or combine
functions from the functions classes with the lowest expected error, and
so on. Many approaches have been proposed in the past for this purpose,
both in the statistics and the machine learning research communities.
Recently in machine learning there has been significant interest in
new techniques for evaluating generalization error, for optimizing generalization
error, and for combining and selecting models. This is exemplified, for
example, by recent work on Structural Risk Minimization, Support Vector
Machines, various Boosting algorithms, and the Bagging algorithm. These
new approaches suggest that better generalization performance can be obtained
using new, broadly applicable procedures. Progress in this area has not
only been important for improving our understanding of how machine learning
algorithms generalize, it has already been demonstrated to be very useful
for practical applications of machine learning and data analysis.
This workshop will bring together several key researchers in the fields
of machine learning and statistics to present their recent results and
debate the controversial issues that have been dividing them in the recent
machine learning and neural network conferences. The following leaders
in this field have accepted to participate to the workshop
as invited speakers:
Peter Bartlett (Australia National University),
Leo Breiman (University of California-Berkeley),
Tom Dietterich (Oregon State University),
Yoav Freund (AT&T Labs-Research),
Michael Perrone (IBM T.J. Watson Research Center),
Robert Schapire (AT&T Labs-Research),
Peter Sollich (King's College, University of London),
Grace Wahba (University of Wisconsin at Madison).
The workshop is sponsored by the CRM
(Centre de Recherches Mathématiques) as well as by the MITACS
(Mathematics of Information Technology And Complex Systems) and IRIS
(Institute for Robotics and Intelligent Systems) Networks of Centers of
Excellence.
Registration and Accomodation
There are no registration fees but participants are urged to register in
advance as the number of attendees may be limited. Follow this link for
registration and accomodation information:
REGISTRATION
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