Gautam Kamath (University of Waterloo)
Abstract:
We consider point estimation and generation of confidence intervals under the constraint of differential privacy. We provide a simple and practical framework for these tasks in relatively general settings. Our investigation addresses a novel challenge that arises in the differentially private setting, which involves the cost of weak a priori bounds on the parameters of interest. This framework is applied to the problems of Gaussian mean and covariance estimation. Despite the simplicity of our method, we are able to achieve minimax near-optimal rates for these problems. Empirical evaluations, on the problems of mean estimation, covariance estimation, and principal component analysis, demonstrate significant improvements in comparison to previous work.
No knowledge of differential privacy will be assumed. Based on joint works with Sourav Biswas, Yihe Dong, Jerry Li, Vikrant Singhal, and Jonathan Ullman.
Speaker
Dr. Gautam Kamath is an Assistant Professor at the University of Waterloo’s Cheriton School of Computer Science, and a faculty affiliate at the Vector Institute. He is mostly interested in principled methods for statistics and machine learning, with a focus on settings which are common in modern data analysis (high-dimensions, robustness, and privacy). He was a Microsoft Research Fellow at the Simons Institute for the Theory of Computing for the Fall 2018 semester program on Foundations of Data Science and the Spring 2019 semester program on Data Privacy: Foundations and Applications. Before that, he completed his Ph.D. at MIT, affiliated with the Theory of Computing group in CSAIL.