L. Lamport, R. Shostak, and M. Pease. The byzantine generals problem. ACM Transactions on Programming Languages and Systems, 4(3):382-401, 1982.
C. Li. Demystifying gpt-3 language model: A technical overview, 2020. "https://lambdalabs.com/blog/demystifying-gpt-3".
N. Loizou, H. Berard, G. Gidel, I. Mitliagkas, and S. Lacoste-Julien. Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity. Advances in Neural Information Processing Systems, 34:19095-19108, 2021.
G. Lugosi and S. Mendelson. Robust multivariate mean estimation: the optimality of trimmed mean. 2021.
L. Lyu, H. Yu, X. Ma, L. Sun, J. Zhao, Q. Yang, and P. S. Yu. Privacy and robustness in federated learning: Attacks and defenses. arXiv preprint arXiv:2012.06337, 2020.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018.
W. R. Mann. Mean value methods in iteration. Proceedings of the American Mathematical Society, 4 (3):506-510, 1953.
P. Mertikopoulos and Z. Zhou. Learning in games with continuous action sets and unknown payoff functions. Mathematical Programming, 173(1):465-507, 2019.
K. Mishchenko, D. Kovalev, E. Shulgin, P. Richtárik, and Y. Malitsky. Revisiting stochastic extragradient. In International Conference on Artificial Intelligence and Statistics, pages 4573-4582. PMLR, 2020.
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida. Spectral normalization for generative adversarial networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net, 2018. URL https://openreview.net/forum?id=B1QRgziT-.
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro. Robust stochastic approximation approach to stochastic programming. SIAM Journal on optimization, 19(4):1574-1609, 2009.
T. Pethick, O. Fercoq, P. Latafat, P. Patrinos, and V. Cevher. Solving stochastic weak minty variational inequalities without increasing batch size. In The Eleventh International Conference on Learning Representations, 2023.
K. Pillutla, S. M. Kakade, and Z. Harchaoui. Robust aggregation for federated learning. IEEE Transactions on Signal Processing, 70:1142-1154, 2022.
L. D. Popov. A modification of the arrow-hurwicz method for search of saddle points. Mathematical notes of the Academy of Sciences of the USSR, 28:845-848, 1980.
M. Ryabinin, E. Gorbunov, V. Plokhotnyuk, and G. Pekhimenko. Moshpit sgd: Communication-efficient decentralized training on heterogeneous unreliable devices. Advances in Neural Information Processing Systems, 34:18195-18211, 2021.
E. K. Ryu and W. Yin. Large-Scale Convex Optimization: Algorithms & Analyses via Monotone Operators. Cambridge University Press, 2022.
C. Song, Z. Zhou, Y. Zhou, Y. Jiang, and Y. Ma. Optimistic dual extrapolation for coherent non-monotone variational inequalities. Advances in Neural Information Processing Systems, 33: 14303-14314, 2020.
S. U. Stich. Unified optimal analysis of the (stochastic) gradient method. arXiv preprint arXiv:1907.04232, 2019.
Q. Tran-Dinh. Sublinear convergence rates of extragradient-type methods: A survey on classical and recent developments. arXiv preprint arXiv:2303.17192, 2023.