[Theory] UC Theory Seminar

[Alexander Razborov]

Samuel Hopkins, PhD
Massachusetts Institute of Technology
 
 
 
 
 
Tuesday, May 16, 2023 at 3:30pm
Kent Chemical Laboratory, Room 120 (New Room)
 
 
Title: Robustness Implies Privacy in Statistical Estimation
 
Abstract:   We study the relationship between adversarial robustness and differential privacy in high-dimensional algorithmic statistics. We give the first black-box reduction from privacy to robustness which can produce private estimators with optimal tradeoffs among sample complexity, accuracy, and privacy for a wide range of fundamental high-dimensional parameter estimation problems, including mean and covariance estimation. We show that this reduction can be implemented in polynomial time in some important special cases. In particular, using nearly-optimal polynomial-time robust estimators for the mean and covariance of high-dimensional Gaussians which are based on the Sum-of-Squares method, we design the first polynomial-time private estimators for these problems with nearly-optimal samples-accuracy-privacy tradeoffs. Our algorithms are also robust to a constant fraction of adversarially-corrupted samples. Joint work with Gautam Kamath, Mahbod Majid, and Shyam Narayanan, to appear in STOC 2023.
 

Bio:  Samuel Hopkins is an Assistant Professor in EECS at MIT. His research focuses on algorithms, optimization, and theoretical machine learning, especially through the lens of convex programming relaxations. He received his PhD in Computer Science from Cornell, and his BS in Computer Science and Mathematics from the University of Washington. 
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