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.