Workshop on Advances in Machine Learning, Montreal, June 2-6, 2003

[Balazs Kegl]

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                         Call for papers
             Workshop on Advances in Machine Learning

                Montreal, Canada, June 2-6, 2003
         URL: www.iro.umontreal.ca/~lisa/workshop2003.html

Organizers: 

  Yoshua Bengio, Balazs Kegl (University of Montreal) 
  Doina Precup (McGill University)


Scope: 

Probabilities are at the core of recent advances in the theory and
practice of machine learning algorithms. The workshop will focus on
three broad areas where these advances are crucial: statistical
learning theory, learning algorithms, and reinforcement learning. The
workshop will therefore bring together experts from each of these
three important domains. Among the sub-topics that will be covered, we
note: variational methods, graphical models, the curse of
dimensionality, empirical methods to take advantage of theories of
generalization error, and some of the applications of these new
methods.

On the theoretical side, in recent years a lot of effort has been
devoted to explain the generalization abilities of popular learning
algorithms such as voting classifiers and kernel methods. Some of
these results have given rise to general principles that can guide
practical classifier design. Some (non-exclusive) sub-topics in this
aspect of the workshop include Rademacher and Gaussian complexities,
algorithmic stability and generalization, localized complexities and
results on the generalization ability of voting classifiers and
kernel-based methods.

On the algorithmic side, one of the emphasis of recent years has been
on probabilistic models that attempt to capture the complex structure
in the data, often by discovering the main lower-dimensional features
that explain the data. This raises interesting and difficult questions
on how to train such models, but such algorithms may have wide ranging
applications in domains in which the data has interesting structure
that may be explained at multiple levels, such as in vision and
language.

In reinforcement learning (RL), recent research has brought
significant advances in some of the traditional problems, such as
understanding the interplay between RL algorithms and function
approximation, and extending RL beyond MDPs. At the same time, new
areas of research, such as computational game theory, have developed
at the interface between RL and probabilistic learning methods. In
this workshop, we invite presentations on all RL topics, ranging from
theoretical development to practical applications.

Invited speakers: 

Rich Sutton, U. Massachusetts, MA, USA 
Andy Barto, U. Massachusetts, MA, USA (to confirm) 
Satinder Singh, U. Michigan, Ann Arbour, MI, USA 
Michael Littman, Rutgers U., NJ, USA 
Leslie Pack Kaelbling, MIT (to confirm) 
Michael Kearns, U. Pennsylvania (to confirm) 
Sridhar Mahadevan, U. Massachusetts 
Peter Bartlett, U. California Berkeley, CA, USA 
Gabor Lugosi, Pompeu Fabra Univ., Spain (to confirm) 
Vladimir Koltchinskii, U. New Mexico, NM, USA 
Yann Le Cun, NEC Research, NJ, USA 
Paolo Frasconi, U. Firenze, Italy 
Dale Schuurmans, Waterloo U., Ontario, Canada 
Nando de Freitas, U. British Columbia, BC, Canada 
Sam Roweis, U. Toronto, Ontario, Canada 
Geoff Hinton, U. Toronto, Ontario, Canada 

Important dates: 

March 31, Paper submission deadline 
April 15, Notification of paper acceptance/rejection. 

Submission: 

Papers should be submitted electronically to kegl at iro.umontreal.ca. 
Papers can be submitted either as a postscript or a pdf (acrobat) file. 
No proceedings are currently planned. 

Registration: 

The registration fees are minimal: regular registration fees are
100$CAN. Reduced rate for students from a Canadian academic
institution: 50$CAN. 

Venue: 

The workshop will take place at the Centre de Recherches
Mathematiques, on the campus of Universite de Montreal, in lively and
beautiful Montreal, Canada. The conference will be held in the
Pavillon Andre Aisenstadt, 2920 chemin de la Tour.