Abstract:
We will discuss a new set of techniques for stable statistical estimation, leading to fast and near-optimal private algorithms for mean estimation, covariance estimation, and linear regression. The analysis proceeds by constructing a stabilizing wrapper around a greedy outlier-removal process. We will also discuss connections with a recent line of work on robustness and auditing.
Based on work with Sam Hopkins, Adam Smith, Jon Hayase, Xiyang Liu, Weihao Kong, Sewoong Oh, and Juan Perdomo.
Bio:
Gavin Brown is an Assistant Professor at the University of Wisconsin–Madison, in the Department of Computer Sciences. He was a postdoctoral scholar at the University of Washington, where he was supervised by Sewoong Oh. He received his PhD from Boston University, where he was advised by Adam Smith.