Speaker: Ryan Tibshirani

Abstract: I'll give a high-level intro to some basic in random matrix theory, primarily surrounding the Marchenko-Pastur law. Then I'll talk about how this and other more recent results in random matrix theory can be leveraged to understand the asymptotics of ridge regression in a very general and interesting way, due to Dobriban and Wager (2018) https://arxiv.org/pdf/1507.03003.pdf. Time permitting, I'll also mention some recent work by Alnur Ali, Zico Kolter, and myself, where we use similar techniques to develop an understanding of gradient flow (continuous-time gradient descent) and its relationship to ridge.