The Taub Faculty of Computer Science Events and Talks

Linear classification is a fundamental problem of machine learning, in which positive and negative examples of a concept are represented in Euclidean space by their feature vectors, and we seek to find a hyperplane separating the two classes of vectors.

In this talk we'll describe recent advances in efficient algorithms for linear classification and related machine learning problems. In particular we'll describe the first sublinear-time (information-optimal) algorithms for linear classification and discuss when can optimization time meets the information limit.