Title: Multi-task Learning and Matrix Regularization
Speaker: Prof. Massimiliano Pontil
Affiliation: Department of Computer Science – University College London
Location: Room 301 William Penney
Time: 2:00pm

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 these methods to computer vision and human computer interaction.

About the speaker. Massimiliano Pontil is Professor of Computational Statistics and Machine Learning in the Department of Computer Science at University College London. 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.