[54] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018.
[55] Panayotis Mertikopoulos, Christos Papadimitriou, and Georgios Piliouras. Cycles in adversarial regularized learning. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, pages 2703-2717. SIAM, 2018.
[56] Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. The numerics of GANs. Advances in Neural Information Processing Systems, 30, 2017.
[57] Aryan Mokhtari, Asuman Ozdaglar, and Sarath Pattathil. A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach. In International Conference on Artificial Intelligence and Statistics, pages 1497-1507. PMLR, 2020.
[58] Aryan Mokhtari, Asuman E Ozdaglar, and Sarath Pattathil. Convergence rate of  $O(1 / k)$  for optimistic gradient and extragradient methods in smooth convex-concave saddle point problems. SIAM Journal on Optimization, 30(4):3230-3251, 2020.
[59] Arkadi Nemirovski. Prox-method with rate of convergence  $O(1 / t)$  for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems. SIAM Journal on Optimization, 15(1):229-251, 2004.
[60] Yurii Nesterov. A method for solving a convex programming problem with convergence rate  $O(1 / k^2)$ . In Soviet Mathematics. Doklady, volume 27, pages 367-372, 1983.
[61] Yurii Nesterov. Introductory lectures on convex optimization: A basic course, volume 87. Springer Science & Business Media, 2003.
[62] Yurii Nesterov and Vladimir Spokoiny. Random gradient-free minimization of convex functions. Foundations of Computational Mathematics, 17:527-566, 2017.
[63] Yuyuan Ouyang and Yangyang Xu. Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems. Mathematical Programming, 185(1-2):1-35, 2021.
[64] Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alché Buc, Emily Fox, and Hugo Larochelle. Improving reproducibility in machine learning research (A report from the NeurIPS 2019 reproducibility program). The Journal of Machine Learning Research, 22(1):7459-7478, 2021.
[65] R Tyrrell Rockafellar. Monotone operators associated with saddle-functions and minimax problems. Nonlinear Functional Analysis, 18(part 1):397-407, 1970.
[66] R Tyrrell Rockafellar. Monotone operators and the proximal point algorithm. SIAM Journal on Control and Optimization, 14(5):877-898, 1976.
[67] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training GANs. Advances in Neural Information Processing Systems, 29, 2016.
[68] Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan. Stochastic convex optimization. In Conference on Learning Theory, volume 2, page 5, 2009.
[69] Gil I Shamir and Lorenzo Coviello. Anti-distillation: Improving reproducibility of deep networks. arXiv preprint arXiv:2010.09923, 2020.
[70] Fedor Stonyakin, Alexander Gasnikov, Pavel Dvurechensky, Alexander Titov, and Mohammad Alkousa. Generalized mirror prox algorithm for monotone variational inequalities: Universality and inexact oracle. Journal of Optimization Theory and Applications, 194(3):988-1013, 2022.
[71] Kiran K, Thekumparampil, Prateek Jain, Praneeth Netrapalli, and Sewoong Oh. Efficient algorithms for smooth minimax optimization. Advances in Neural Information Processing Systems, 32, 2019.