Feb 24, 2020
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, which was the recipient of the NeurIPS 2019 Outstanding Paper award. The paper, which focuses on high-dimensional robust learning, is regarded as the first progress made around distribution-independent learning with noise since the 80s. In our conversation, we explore robustness in machine learning, problems with corrupt data in high-dimensional settings, and of course, a deep dive into the paper.
Check out our full write up on the paper and the interview at twimlai.com/talk/351.