[34] Haipeng Luo and Robert E. Schapire. Achieving all with no parameters: AdaNormalHedge. In Proceedings of The 28th Conference on Learning Theory, volume 40, pages 1286-1304, 2015.
[35] Haipeng Luo, Chen-Yu Wei, and Kai Zheng. Efficient online portfolio with logarithmic regret. In Advances in Neural Information Processing Systems, volume 31, pages 8235-8245, 2018.
[36] Thodoris Lykouris, Vahab Mirrokni, and Renato Paes Leme. Stochastic bandits robust to adversarial corruptions. In Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, pages 114-122, 2018.
[37] Brendan McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and L1 regularization. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, volume 15, pages 525–533, 2011.
[38] Francesco Orabona. A modern introduction to online learning. CoRR, abs/1912.13213, 2019. URL http://arxiv.org/abs/1912.13213.
[39] Antonio Piccolboni and Christian Schindelhauer. Discrete prediction games with arbitrary feedback and loss (extended abstract). In Computational Learning Theory, pages 208-223, 2001.
[40] Roman Pogodin and Tor Lattimore. On first-order bounds, variance and gap-dependent bounds for adversarial bandits. In Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, volume 115 of Proceedings of Machine Learning Research, pages 894-904, 2020.
[41] Aldo Rustichini. Minimizing regret: The general case. Games and Economic Behavior, 29(1): 224-243, 1999.
[42] Aadirupa Saha and Pierre Gaillard. Versatile dueling bandits: Best-of-both world analyses for learning from relative preferences. In Proceedings of the 39th International Conference on Machine Learning, volume 162, pages 19011–19026, 2022.
[43] Yevgeny Seldin and Gábor Lugosi. An improved parametrization and analysis of the EXP3++ algorithm for stochastic and adversarial bandits. In Proceedings of the 2017 Conference on Learning Theory, volume 65, pages 1743-1759, 2017.
[44] Yevgeny Seldin and Aleksandrs Slivkins. One practical algorithm for both stochastic and adversarial bandits. In Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research, pages 1287-1295, 2014.
[45] Taira Tsuchiya, Junya Honda, and Masashi Sugiyama. Analysis and design of Thompson sampling for stochastic partial monitoring. In Advances in Neural Information Processing Systems, volume 33, pages 8861-8871, 2020.
[46] Taira Tsuchiya, Shinji Ito, and Junya Honda. Best-of-both-worlds algorithms for partial monitoring. In Proceedings of The 34th International Conference on Algorithmic Learning Theory, 2023.
[47] Taira Tsuchiya, Shinji Ito, and Junya Honda. Further adaptive best-of-both-worlds algorithm for combinatorial semi-bandits. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, pages 8117–8144, 2023.
[48] Hastagiri P Vanchinathan, Gábor Bartók, and Andreas Krause. Efficient partial monitoring with prior information. In Advances in Neural Information Processing Systems, volume 27, pages 1691-1699, 2014.
[49] Chen-Yu Wei and Haipeng Luo. More adaptive algorithms for adversarial bandits. In Proceedings of the 31st Conference On Learning Theory, volume 75, pages 1263-1291, 2018.
[50] Chen-Yu Wei, Yi-Te Hong, and Chi-Jen Lu. Tracking the best expert in non-stationary stochastic environments. In Advances in Neural Information Processing Systems, volume 29, pages 3972–3980, 2016.