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Minimax problems of the form $\min_x \max_y \Psi(x,y)$ have attracted increased interest largely due to advances in machine learning, in particular generative adversarial networks.
The extragradient method for finding saddle points and other problems
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Generative adversarial imitation learning
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Stochastic gradient methods for distributionally robust optimization with f-divergences
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Robust optimization for non-convex objectives
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Improved training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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The numerics of GANs
L. Mescheder, S. Nowozin, and A. Geiger · 2017
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The space of transferable adversarial examples
F. Tramèr, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2017
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Training GANs with optimism
C. Daskalakis, A. Ilyas, V. Syrgkanis, and H. Zeng · 2018
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The limit points of (optimistic) gradient descent in min-max optimization
C. Daskalakis and I. Panageas · 2018
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D. Davis and D. Drusvyatskiy · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Finite regret and cycles with fixed step-size via alternating gradient descent-ascent
J. P. Bailey, G. Gidel, and G. Piliouras · 2020
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What is local optimality in nonconvex-nonconcave minimax optimization?
C. Jin, P. Netrapalli, and M. I. Jordan · 2020
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Near-optimal algorithms for minimax optimization
T. Lin, C. Jin, and M. I. Jordan · 2020
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On gradient descent ascent for nonconvex-concave minimax problems
T. Lin, C. Jin, and M. I. Jordan · 2020
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A decentralized parallel algorithm for training generative adversarial nets
M. Liu, W. Zhang, Y. Mroueh, X. Cui, J. Ross, T. Yang, and P. Das · 2020
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S. Lu, I. Tsaknakis, M. Hong, and Y. Chen · 2020
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L. Mescheder, A. Geiger, and S. Nowozin · 2018
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N. Papernot, F. Faghri, N. Carlini, I. Goodfellow, R. Feinman, A. Kurakin, C. Xie, Y. Sharma, T. Brown, A. Roy, A. Matyasko, V. Behzadan, K. Hambardzumyan, Z. Zhang, Y.-L. Juang, Z. Li, R. Sheatsley, A. Garg, J. Uesato, W. Gierke, Y. Dong, D. Berthelot, P. Hendricks, J. Rauber, and R. Long · 2018
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Non-convex min-max optimization: Provable algorithms and applications in machine learning
H. Rafique, M. Liu, Q. Lin, and T. Yang · 2018
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Certifiable distributional robustness with principled adversarial training
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A. Akbik, T. Bergmann, D. Blythe, K. Rasul, S. Schweter, and R. Vollgraf · 2019
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On the global convergence of imitation learning: A case for linear quadratic regulator
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Hybrid variance-reduced SGD algorithms for nonconvex-concave minimax problems
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