»ã±¨±êÌâ (Title)£ºData-Driven Minimax Optimization with Expectation Constraints

»ã±¨ÈË (Speaker)£ºÛªÐñ¶« ×êÑÐÔ±£¨¸´µ©´óѧ´óÊý¾ÝѧԺ£©

»ã±¨¹¦·ò (Time)£º2023Äê11ÔÂ7ÈÕ (Öܶþ) 09:40

»ã±¨µØÖ· (Place)£ºÐ£±¾²¿GJ303

Ô¼ÇëÈË(Inviter)£ºÐì×Ë ½ÌÊÚ

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»ã±¨ÌáÒª£ºAttention to data-driven optimization approaches has grown significantly over recent decades, but data-driven constraints have rarely been studied. In this talk, we focus on the non-smooth convex-concave stochastic minimax regime and formulate the data-driven constraints as expectation constraints. Then, we propose a class of efficient primal-dual algorithms to tackle the minimax optimization with expectation constraints, and show that our algorithms converge at the optimal rate of $\mathcal O(\frac{1}{\sqrt{N}})$, where $N$ is the number of iterations. We also verify the practical efficiency of our algorithms by conducting numerical experiments on large-scale real-world applications.