Scalable and Provably Robust Algorithms for Machine Learning

Zoom link for remote participants, passcode:  256561

Abstract: As machine learning plays a more prominent role in our society, we need learning algorithms that are reliable and robust.  It is important to understand whether existing algorithms are robust against adversarial attacks and design new robust solutions that work under weaker assumptions.  The long-term goal is to bridge the gap between the growing need for robust algorithms and the lack of systematic understanding of robustness.

In this talk, I will discuss the challenges that arise in the design and analysis of robust algorithms for machine learning.  I will focus on three lines of my recent work: (1) designing faster and simpler algorithms for high-dimensional robust statistics where a small fraction of the input data is arbitrarily corrupted, (2) analyzing the optimization landscape of non-convex approaches for low-rank matrix problems and making non-convex optimization robust against semi-random adversaries, and (3) considering learning in the presence of strategic behavior where the goal is to design good algorithms that account for the agents’ strategic responses.

Bio: Yu Cheng is an assistant professor in the Mathematics department at the University of Illinois at Chicago.  He obtained his Ph.D. in Computer Science from the University of Southern California.  Before joining UIC, he was a postdoc at Duke University and a visiting member at the Institute for Advanced Study.  His main research interests include machine learning, optimization, and game theory.