Speaker: Yu Cheng (https://homepages.math.uic.edu/~yucheng/
Time: 03/26/21 11:45am PST
We study the fundamental problem of high-dimensional robust estimation where a constant fraction of the samples are adversarially corrupted. Recent work gave the first polynomial-time robust algorithms for basic statistical problems with dimension-independent error guarantees.
In this talk, we will discuss several recent results in high-dimensional robust statistics, focusing on (1) designing robust estimators that run as fast as their non-robust counterparts, and (2) exploring the optimization landscape of more direct non-convex formulations of robust estimation.
Most of the talk is based on joint work with Ilias Diakonikolas, Rong Ge, and Mahdi Soltanolkotabi.