[17] A. R. Cardoso, J. D. Abernethy, H. Wang, and H. Xu. Competing against nash equilibria in adversarially changing zero-sum games. In International Conference on Machine Learning (ICML), volume 97 of Proceedings of Machine Learning Research, pages 921-930. PMLR, 2019.
[18] N. Cesa-Bianchi, P. Gaillard, G. Lugosi, and G. Stoltz. Mirror descent meets fixed share (and feels no regret). In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS), pages 989–997, 2012.
[19] X. Chen, X. Deng, and S.-H. Teng. Settling the complexity of computing two-player Nash equilibria. Journal of the ACM, 2009.
[20] C.-K. Chiang, T. Yang, C.-J. Lee, M. Mahdavi, C.-J. Lu, R. Jin, and S. Zhu. Online optimization with gradual variations. In Conference on Learning Theory, pages 6-1, 2012.
[21] P. L. Combettes and T. Pennanen. Proximal methods for cohypomonotone operators. SIAM J. Control. Optim., pages 731-742, 2004.
[22] L. Crippa, Y. Gur, and B. Light. Equilibria in repeated games under no-regret with dynamic benchmarks. CoRR, abs/2212.03152, 2023.
[23] C. D. Dang and G. Lan. On the convergence properties of non-euclidean extragradient methods for variational inequalities with generalized monotone operators. Comput. Optim. Appl., pages 277-310, 2015.
[24] C. Daskalakis. Non-concave games: A challenge for game theory's next 100 years, 2022.
[25] C. Daskalakis, P. Goldberg, and C. Papadimitriou. The complexity of computing a Nash equilibrium. SIAM Journal on Computing, 2008.
[26] C. Daskalakis, A. Deckelbaum, and A. Kim. Near-optimal no-regret algorithms for zero-sum games. In Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2011.
[27] C. Daskalakis, A. Ilyas, V. Syrgkanis, and H. Zeng. Training gans with optimism. In 6th International Conference on Learning Representations, ICLR. OpenReview.net, 2018.
[28] C. Daskalakis, D. J. Foster, and N. Golowich. Independent policy gradient methods for competitive reinforcement learning. In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS), 2020.
[29] C. Daskalakis, M. Fishelson, and N. Golowich. Near-optimal no-regret learning in general games. In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS), pages 27604-27616, 2021.
[30] A. Deligkas, J. Fearney, A. Hollender, and T. Melissourgos. Pure-circuit: Strong inapproximability for PPAD. In Proceedings of the Annual Symposium on Foundations of Computer Science (FOCS), pages 159-170. IEEE, 2022.
[31] Y. Deng, V. Mirrokni, and S. Zuo. Non-clairvoyant dynamic mechanism design with budget constraints and beyond. In Proceedings of the 22nd ACM Conference on Economics and Computation, Proceedings of the ACM Conference on Economics and Computation (EC). Association for Computing Machinery, 2021.
[32] J. Diakonikolas. Halpern iteration for near-optimal and parameter-free monotone inclusion and strong solutions to variational inequalities. In J. D. Abernethy and S. Agarwal, editors, Conference on Learning Theory (COLT), volume 125 of Proceedings of Machine Learning Research, pages 1428-1451. PMLR, 2020.
[33] J. Diakonikolas, C. Daskalakis, and M. I. Jordan. Efficient methods for structured nonconvex-nonconcave min-max optimization. In The 24th International Conference on Artificial Intelligence and Statistics, AISTATS, volume 130 of Proceedings of Machine Learning Research, pages 2746-2754. PMLR, 2021.