Exploring Robustness in High-Dimensional Statistics

Today we’re joined by Ilias Diakonikolas, faculty in the CS department at the University of Wisconsin-Madison, and author of the paper Distribution-Independent PAC Learning of Halfspaces with Massart Noise, recipient of the NeurIPS 2019 Outstanding Paper award. The paper is regarded as the first progress made around distribution-independent learning with noise since the 80s. In our conversation, we explore robustness in ML, problems with corrupt data in high-dimensional settings, and of course, the paper.