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The use of min-max optimization in adversarial training of deep neural network classifiers and training of generative adversarial networks has motivated the study of nonconvex-nonconcave optimization objectives, which frequently arise in these applications.
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The limit points of (optimistic) gradient descent in min-max optimization
Constantinos Daskalakis and Ioannis Panageas · 2018
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Training GANs with optimism
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2018
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Optimal affine-invariant smooth minimization algorithms
Alexandre d’Aspremont, Cristobal Guzman, and Martin Jaggi · 2018
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A primal-dual algorithm for general convex-concave saddle point problems
Erfan Yazdandoost Hamedani and Necdet Serhat Aybat · 2018
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Qihang Lin, Mingrui Liu, Hassan Rafique, and Tianbao Yang · 2018
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Cycles in adversarial regularized learning
Panayotis Mertikopoulos, Christos H. Papadimitriou, and Georgios Piliouras · 2018
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Last-iterate convergence rates for min-max optimization
Jacob Abernethy, Kevin A Lai, and Andre Wibisono · 2019
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Optimal algorithms for stochastic three-composite convex-concave saddle point problems
Renbo Zhao · 2019
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A tight and unified analysis of extragradient for a whole spectrum of differentiable games
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Generalized monotone operators and their averaged resolvents
Heinz H Bauschke, Walaa M Moursi, and Xianfu Wang · 2020
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Independent policy gradient methods for competitive reinforcement learning
Constantinos Daskalakis, Dylan J Foster, and Noah Golowich · 2020
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Halpern iteration for near-optimal and parameter-free monotone inclusion and strong solutions to variational inequalities
Jelena Diakonikolas · 2020
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Last-iterate convergence: Zero-sum games and constrained min-max optimization
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Global convergence and variance-reduced optimization for a class of nonconvex-nonconcave minimax problems
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