Omar Montassir : Learning Predictors Robust to Adversarial Examples: Complexity and Algorithms

Posted by Karoline Dubin , part of the Computer Science Theory Seminar.

At

Feb. 16, 2022, 4 p.m.

In

636 SEO

Abstract

In this talk, we will discuss the problem of learning an adversarially robust predictor from i.i.d. training data. That is, learning a predictor that performs well not only on future i.i.d. test instances, but also on adversarial perturbations of these instances. There has been much empirical interest in this question, and in this talk we will take a theoretical perspective and see how it leads to practically relevant insights, including: the need to depart from a (robust) empirical risk minimization approach, and thinking of what kind of accesses and reductions can allow provable learning guarantees. Based on joint work with Steve Hanneke and Nati Srebro.