[6] Onur Atan, William R Zame, and Mihaela Schaar. Sequential patient recruitment and allocation for adaptive clinical trials. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 1891-1900. PMLR, 2019.
[7] Susan Athey and Stefan Wager. Policy learning with observational data. *Econometrica*, 89(1):133-161, 2021.
[8] Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, and Sanjay Shakkottai. Combinatorial blocking bandits with stochastic delays. In International Conference on Machine Learning, pages 404-413. PMLR, 2021.
[9] Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine learning, 47(2-3):235-256, 2002.
[10] Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire. The nonstochastic multiarmed bandit problem. SIAM journal on computing, 32(1):48-77, 2002.
[11] Akshay Balsubramani, Zohar Karnin, Robert E Schapire, and Masour Zoghi. Instance-dependent regret bounds for dueling bandits. In Conference on Learning Theory, pages 336-360. PMLR, 2016.
[12] Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, and Sanjay Shakkottai. Contextual blocking bandits. In International Conference on Artificial Intelligence and Statistics, pages 271-279. PMLR, 2021.
[13] Soumya Basu, Rajat Sen, Sujay Sanghavi, and Sanjay Shakkottai. Blocking bandits. Advances in Neural Information Processing Systems, 32, 2019.
[14] Donald A Berry. Bayesian clinical trials. Nature reviews Drug discovery, 5(1):27-36, 2006.
[15] Omar Besbes, Yonatan Gur, and Assaf Zeevi. Stochastic multi-armed-bandit problem with non-stationary rewards. In Advances in neural information processing systems, pages 199–207, 2014.
[16] Omar Besbes, Yonatan Gur, and Assaf Zeevi. Non-stationary stochastic optimization. Operations research, 63(5):1227-1244, 2015.
[17] Omar Besbes, Yonatan Gur, and Assaf Zeevi. Optimal exploration-exploitation in a multi-armed bandit problem with non-stationary rewards. Stochastic Systems, 9(4):319-337, 2019.
[18] Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, and Julien Seznec. Efficient change-point detection for tackling piecewise-stationary bandits. The Journal of Machine Learning Research, 23(1):3337-3376, 2022.
[19] Nikhil Bhat, Vivek F Farias, Ciamac C Moallemi, and Deeksha Sinha. Near-optimal ab testing. Management Science, 66(10):4477-4495, 2020.
[20] Nicholas Bishop, Hau Chan, Debmalya Mandal, and Long Tran-Thanh. Adversarial blocking bandits. Advances in Neural Information Processing Systems, 33:8139–8149, 2020.
[21] Iavor Bojinov, David Simchi-Levi, and Jinglong Zhao. Design and analysis of switchback experiments. Management Science, 2022.
[22] Yang Cao, Zheng Wen, Branislav Kveton, and Yao Xie. Nearly optimal adaptive procedure with change detection for piecewise-stationary bandit. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 418-427. PMLR, 2019.
[23] Ningyuan Chen, Xuefeng Gao, and Yi Xiong. Debiasing samples from online learning using bootstrap. In International Conference on Artificial Intelligence and Statistics, pages 8514-8533. PMLR, 2022.
[24] Wang Chi Cheung, David Simchi-Levi, and Ruihao Zhu. Hedging the drift: Learning to optimize under nonstationarity. Management Science, 68(3):1696-1713, 2022.