[13] Noam Brown, Anton Bakhtin, Adam Lerer, and Qucheng Gong. Combining deep reinforcement learning and search for imperfect-information games. In Conference on Neural Information Processing Systems (NeurIPS), 2020.
[14] Yang Cai, Argyris Oikonomou, and Weiqiang Zheng. Finite-time last-iterate convergence for learning in multi-player games. In Conference on Neural Information Processing Systems (NeurIPS), 2022.
[15] Modibo K. Camara, Jason D. Hartline, and Aleck C. Johnsen. Mechanisms for a no-regret agent: Beyond the common prior. In Annual Symposium on Foundations of Computer Science (FOCS), pages 259–270. IEEE, 2020.
[16] Andrea Celli, Alberto Marchesi, Gabriele Farina, and Nicola Gatti. No-regret learning dynamics for extensive-form correlated equilibrium. Conference on Neural Information Processing Systems (NeurIPS), 33:7722-7732, 2020.
[17] Xi Chen, Xiaotie Deng, and Shang-Hua Teng. Settling the complexity of computing two-player Nash equilibria. Journal of the ACM, 2009.
[18] Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, and Shenghuo Zhu. Online optimization with gradual variations. In Conference on Learning Theory (COLT), pages 6-1, 2012.
[19] Francis Chu and Joseph Halpern. On the NP-completeness of finding an optimal strategy in games with common payoffs. International Journal of Game Theory, 2001.
[20] Vincent Conitzer and Dmytro Korzhyk. Commitment to correlated strategies. In AAAI Conference on Artificial Intelligence (AAAI). AAAI Press, 2011.
[21] Vincent Conitzer and Tuomas Sandholm. New complexity results about Nash equilibria. Games and Economic Behavior, 63(2):621-641, 2008. Early version in IJCAI-03.
[22] Constantinos Daskalakis and Ioannis Panageas. Last-iterate convergence: Zero-sum games and constrained min-max optimization. In 10th Innovations in Theoretical Computer Science Conference, ITCS 2019, volume 124 of LIPics, pages 27:1–27:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019.
[23] Constantinos Daskalakis, Paul W Goldberg, and Christos H Papadimitriou. The complexity of computing a nash equilibrium. SIAM Journal on Computing, 39(1), 2009.
[24] Constantinos Daskalakis, Maxwell Fishelson, and Noah Golowich. Near-optimal no-regret learning in general games. In Conference on Neural Information Processing Systems (NeurIPS), pages 27604-27616, 2021.
[25] Yuan Deng, Vahab Mirrokni, and Song Zuo. Non-clairvoyant dynamic mechanism design with budget constraints and beyond. Available at SSRN 3383231, 2019.
[26] Miroslav Dudík and Geoffrey J Gordon. A sampling-based approach to computing equilibria in succinct extensive-form games. In Conference on Uncertainty in Artificial Intelligence (UAI), pages 151-160, 2009.
[27] Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David Parkes, and Sai Srivatsa Ravindranath. Optimal auctions through deep learning. In International Conference on Machine Learning, pages 1706-1715. PMLR, 2019.
[28] Gabriele Farina and Tuomas Sandholm. Polynomial-time computation of optimal correlated equilibria in two-player extensive-form games with public chance moves and beyond. In Conference on Neural Information Processing Systems (NeurIPS), 2020.
[29] Gabriele Farina, Christian Kroer, and Tuomas Sandholm. Regret circuits: Composability of regret minimizers. In International Conference on Machine Learning, pages 1863-1872, 2019.