Wednesday, September 23, 2009
04:30 PM in REC 315
Assistant Professor SVN Vishwanathan
Department of Statistics, Purdue University

Bundle Methods for Regularized Risk Minimization: Upper and lower bounds

Abstract

Machine learning poses data driven optimization problems. Computing the function value and gradients for these problems is challenging because they often involve thousands of variables and millions of training data points. Therefore, a lot of recent research has focused on designing specialized optimization algorithms for such problems. In this talk, I will present a high level overview of one such algorithm that we recently developed. Our algorithm BMRM (Bundle Methods for Regularized Risk Minimization) not only has good practical performance but also sports strong theoretical guarantees, which I will discuss. The talk will be broadly accessible and will have plenty of fun pictures and illustrations!