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berkeley.edu
Zeroth-Order Methods for Convex-Concave Minmax Problems: Learning from Strategically Generated Data
Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data. In this talk, the focus will be on a class of convex-concave min-max problems possessing a finite sum structure, and we will discuss a zero-th order variant of Optimistic Gradient Descent-Ascent (OGDA) that ...
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