[AG12] Shipra Agrawal and Navin Goyal. Analysis of thompson sampling for the multi-armed bandit problem. In Conference on learning theory, pages 39–1. JMLR Workshop and Conference Proceedings, 2012.
[AMTS11] Animashree Anandkumar, Nithin Michael, Ao Kevin Tang, and Ananthram Swami. Distributed algorithms for learning and cognitive medium access with logarithmic regret. IEEE Journal on Selected Areas in Communications, 29(4):731-745, 2011.
[BCB12] Sébastien Bubeck and Nicolo Cesa-Bianchi. Regret analysis of stochastic and non-stochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning, 5(1):1–122, 2012.
[BCZ+97] Donald Berry, Robert Chen, Alan Zame, David Heath, and Larry Shepp. Bandit problems with infinitely many arms. Annals of Statistics, 25(5):2103-2116, 1997.
[BE95] Donald A Berry and Stephen G Eick. Adaptive assignment versus balanced randomization in clinical trials: a decision analysis. Statistics in medicine, 14(3):231-246, 1995.
[BF85] Donald Berry and Bert Fristedt. Bandit problems: sequential allocation of experiments. London: Chapman and Hall, 5(71-87):7-7, 1985.
[BMS09] Sébastien Bubeck, Rémi Munos, and Gilles Stoltz. Pure exploration in multi-armed bandits problems. In International conference on Algorithmic learning theory, pages 23–37. Springer, 2009.
$\left[\mathrm{CGL}^{+}17\right]$  Lijie Chen, Anupam Gupta, Jian Li, Mingda Qiao, and Ruosong Wang. Nearly optimal sampling algorithms for combinatorial pure exploration. In Conference on Learning Theory, pages 482-534. PMLR, 2017.
[CK18] Arghya Roy Chaudhuri and Shivaram Kalyanakrishnan. Quantile-regret minimisation in infinitely many-armed bandits. In UAI, pages 425-434, 2018.
[CK19] Arghya Roy Chaudhuri and Shivaram Kalyanakrishnan. PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits. In International Conference on Machine Learning, pages 991-1000. PMLR, 2019.
[CL15] Lijie Chen and Jian Li. On the optimal sample complexity for best arm identification. arXiv preprint arXiv:1511.03774, 2015.
[CL16] Alexandra Carpentier and Andrea Locatelli. Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem. In Conference on Learning Theory, pages 590-604, 2016.
[DA92] Alejandro De Acosta. Moderate deviations and associated Laplace approximations for sums of independent random vectors. Transactions of the American Mathematical Society, 329(1):357-375, 1992.
[dHCMC21] Rianne de Heide, James Cheshire, Pierre Menard, and Alexandra Carpentier. Bandits with many optimal arms. Advances in Neural Information Processing Systems, 34:22457-22469, 2021.
[EDMM02] Eyal Even-Dar, Shie Mannor, and Yishay Mansour. PAC bounds for multi-armed bandit and Markov decision processes. In International Conference on Computational Learning Theory, pages 255–270. Springer, 2002.
[EDMM06] Eyal Even-Dar, Shie Mannor, and Yishay Mansour. Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems. Journal of machine learning research, 7(6), 2006.
[GHRZ19] Zijun Gao, Yanjun Han, Zhimei Ren, and Zhengqing Zhou. Batched Multi-Armed Bandits Problem. Advances in Neural Information Processing Systems, 32, 2019.
[GM14] Spencer Greenberg and Mehryar Mohri. Tight Lower Bound on the Probability of a Binomial Exceeding its Expectation. Statistics & Probability Letters, 86:91-98, 2014.
[GM20] Ofer Grossman and Dana Moshkovitz. Amplification and Derandomization without Slowdown. SIAM Journal on Computing, 49(5):959-998, 2020.
[Hoe94] Wassily Hoeffding. Probability inequalities for sums of bounded random variables. In The collected works of Wassily Hoeffding, pages 409-426. Springer, 1994.