Web: http://arxiv.org/abs/2209.10825

Sept. 23, 2022, 1:11 a.m. | Jiajin Li, Linglingzhi Zhu, Anthony Man-Cho So

cs.LG updates on arXiv.org arxiv.org

Nonconvex-concave minimax optimization has received intense interest in
machine learning, including learning with robustness to data distribution,
learning with non-decomposable loss, adversarial learning, to name a few.
Nevertheless, most existing works focus on the gradient-descent-ascent (GDA)
variants that can only be applied in smooth settings. In this paper, we
consider a family of minimax problems whose objective function enjoys the
nonsmooth composite structure in the variable of minimization and is concave in
the variables of maximization. By fully exploiting the …

arxiv math minimax optimization

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