[Lisa_seminaires] ML talk tomorrow by François Laviolette

Yoshua Bengio yoshua.umontreal at gmail.com
Mar 14 Juin 15:59:49 EDT 2016


Speaker: François Laviolette
Wednesday (15th June) at 11h00.
Room: TBA

*1.**- TITLE: **Sparsity, interpretability and sample compression in
Machine Learning*


*2.- ABSTRACT:*
We will investigate the Machine Learning subject called the Fat Data
paradigm, a setting where the dimension of the feature space is much bigger
than the number of training examples. This is a situation often encountered
in data arising from life science. In such situations, there is a real risk
of overfitting, even if one makes use of highly regularized learning
algorithms. To overcome the lack of examples and even achieve good
generalization performances, one solution consists in including prior
knowledge of the domain into the learning algorithm. Another approach is to
look for predictors that will be interpretable by experts in the domain,
who in turn will help to validate the prediction.
The Set Covering Machine, introduced by Mario Marchand and John
Shawe-Taylor more than a decade ago, is a learning algorithm that can be
used to produce such interpretable predictors. We will present the original
version of this algorithm together with a new one, specialized for genomic
data. We will also show some results, based on the sample compression
theory, that show why such a learning algorithm can alleviate overfitting,
even in the fat data situation.

*3.- Bio Sketch*
François Laviolette is a full Professor at the department of Computer
Science and Software Engineering of Laval University. He received his
doctorate in graph theory at the University of Montreal in 1995. His thesis
solved an old problem that had been studied among others by the
mathematician Paul Erdos. For over 10 years, his main area of research has
been Machine Learning. More specifically, he develops learning algorithms
to solve new types of learning problems, including problems relating to
genomics and proteomics. He is currently the director of the new Big Data
Research Center at Laval University.
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