Empirical Risk Minimization in the Non-interactive LDP Model

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https://kaust.zoom.us/j/99005716923

Abstract

As a fundamental problem in both machine learning and privacy, Empirical Risk Minimization in the Differential Privacy Model (DP-ERM) received much attentions. However, most of the previous studies are either in the central DP model or interactive LDP model. In this talk, I will discuss some recent developments of DP-ERM in the non-interactive LDP model. 

Brief Biography

Di Wang is currently an Assistant Professor at the King Abdullah University of Science and Technology (KAUST). Before that, he got his PhD degree in the Computer Science and Engineering at the State University of New York (SUNY) at Buffalo. And he obtained his BS and MS degrees in mathematics from Shandong University and the University of Western Ontario, respectively. His research areas include differentially private machine learning,  adversarial machine learning, interpretable machine learning, robust estimation and optimization.

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