Multi-task Learning

With Prof. Massimiliano Pontil, University College of London

Abstract:

We discuss the problem of estimating a structured matrix with a large number of elements. A key motivation for this problem occurs in multi-task learning. In this case, the columns of the matrix correspond to the parameters of different regression or classification tasks, and there is structure due to relations between the tasks. We present a general method to learn the tasks' parameters as well as their structure. Our approach is based on solving a convex optimization problem, involving a data term and a penalty term. We highlight different types of penalty terms which are of practical and theoretical importance. They implement structural relations between the tasks and achieve a sparse representations of parameters. We address computational issues as well as the predictive performance of the method. Finally, we describe some recent applications of the above methods to computer vision and human computer interaction.

Short Bio:

Massimiliano Pontil is Professor of Computational Statistics and Machine Learning in the Department of Computer Science at University College London. He received a Laurea degree and a PhD in Physics from the University of Genova in 1994 and 1999, respectively. His research interests are in the field of machine learning with a focus on regularization methods, convex optimization and statistical estimation. He has published about 100 research papers on these topics, is regularly in the programme committee of the leading conferences in the field, is an associate editor of the Machine Learning Journal and is a member of the scientific advisory board of the Max Planck Institute for Biological Cybernetics, Germany.

Date: Monday October 22th, 2012, 12:00pm

Location: Battelle bât D, room D185

5 octobre 2012

À la Une