[Lisa_seminaires] ML talk tomorrow by François Laviolette

Yoshua Bengio yoshua.umontreal at gmail.com
Mar 14 Juin 16:57:28 EDT 2016


The conference will be in room 5441, Pavillon André-Aisenstadt, 11h,
tomorrow Wednesday.

This is organized by Andrea Lodi.

2016-06-14 15:59 GMT-04:00 Yoshua Bengio <yoshua.umontreal at gmail.com>:

> 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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