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Abstract
Min-max optimization plays a critical role in emerging machine learning applications from training GANs to robust reinforcement learning, and from adversarial training to fairness. In this talk, we discuss some recent results on min-max optimization algorithms with a special focus on their adaptivity and reproducibility, besides convergence guarantees.