Surprises in binary linear classification
Andrea Montanari, John D. and Sigrid Banks Professor in Statistics and Mathematics, Stanford University
Abstract: Machine learning calls into question our understanding of statistical methodology, both because of the new classes of statistical models used, and because of the new regimes and use cases. I will focus on the latter aspect by considering the (supposedly) well understood case of binary linear classification. I will discuss a certain number of phenomena that are not captured by classical statistical theory: interpolation, universality, data subsampling, tractability. High-dimensional asymptotics will be used to shed light on these behaviors.
Cost: free
Kisa Kowal
(847) 491-3974
Email