[19] TJJOPS Higuchi. Mechanism of sustained-action medication. theoretical analysis of rate of release of solid drugs dispersed in solid matrices. Journal of pharmaceutical sciences, 52(12):1145-1149, 1963.
[20] Qining Zhang, Honghao Wei, Weina Wang, and Lei Ying. On low-complexity quickest intervention of mutated diffusion processes through local approximation. In Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, pages 141–150, 2022.
[21] Jeremy J Siegel. Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. McGraw-Hill Education, 2021.
[22] Aurélien Garivier, Tor Lattimore, and Emilie Kaufmann. On explore-then-commit strategies. Advances in Neural Information Processing Systems, 29, 2016.
[23] Tianyuan Jin, Pan Xu, Xiaokui Xiao, and Quanquan Gu. Double explore-then-commit: Asymptotic optimality and beyond. In Conference on Learning Theory, pages 2584–2633. PMLR, 2021.
[24] Rémy Degenne, Thomas Nedelec, Clément Calauzènes, and Vianney Perchet. Bridging the gap between regret minimization and best arm identification, with application to a/b tests. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 1988-1996. PMLR, 2019.
[25] Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, and Stuart Russell. Bridging offline reinforcement learning and imitation learning: A tale of pessimism. Advances in Neural Information Processing Systems, 34:11702-11716, 2021.
[26] Gen Li, Cong Ma, and Nathan Srebro. Pessimism for offline linear contextual bandits using lp confidence sets. arXiv preprint arXiv:2205.10671, 2022.
[27] Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvari, and Dale Schuurmans. On the optimality of batch policy optimization algorithms. In International Conference on Machine Learning, pages 11362-11371. PMLR, 2021.
[28] Gen Li, Laixi Shi, Yuxin Chen, Yuejie Chi, and Yuting Wei. Settling the sample complexity of model-based offline reinforcement learning. arXiv preprint arXiv:2204.05275, 2022.
[29] Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, and Yuejie Chi. Pessimistic q-learning for offline reinforcement learning: Towards optimal sample complexity. arXiv preprint arXiv:2202.13890, 2022.
[30] Ying Jin, Zhuoran Yang, and Zhaoran Wang. Is pessimism provably efficient for offline rl? In International Conference on Machine Learning, pages 5084-5096. PMLR, 2021.
[31] Dan Siroker and Pete Koomen. A/B testing: The most powerful way to turn clicks into customers. John Wiley & Sons, 2015.
[32] Emilie Kaufmann, Olivier Cappé, and Aurélien Garivier. On the complexity of best arm identification in multi-armed bandit models. Journal of Machine Learning Research, 17:1–42, 2016.
[33] Emilie Kaufmann, Olivier Cappé, and Aurélien Garivier. On bayesian upper confidence bounds for bandit problems. In Artificial intelligence and statistics, pages 592-600. PMLR, 2012.
[34] Daniel J Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen, et al. A tutorial on thompson sampling. Foundations and Trends® in Machine Learning, 11(1):1-96, 2018.
[35] Daniel Russo and Benjamin Van Roy. An information-theoretic analysis of thompson sampling. The Journal of Machine Learning Research, 17(1):2442-2471, 2016.
[36] Aurélien Garivier and Emilie Kaufmann. Optimal best arm identification with fixed confidence. In Conference on Learning Theory, pages 998-1027. PMLR, 2016.