[20] Tor Lattimore. A lower bound for linear and kernel regression with adaptive covariates. In The Thirty Sixth Annual Conference on Learning Theory, pages 2095-2113. PMLR, 2023.
[21] Tor Lattimore and Csaba Szepesvari. The end of optimism? an asymptotic analysis of finite-armed linear bandits. In Artificial Intelligence and Statistics, pages 728-737. PMLR, 2017.
[22] Lihong Li, Wei Chu, John Langford, and Robert E Schapire. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web, pages 661-670, 2010.
[23] Lihong Li, Yu Lu, and Dengyong Zhou. Provably optimal algorithms for generalized linear contextual bandits. In International Conference on Machine Learning, pages 2071-2080. PMLR, 2017.
[24] Peng Liao, Kristjan Greenewald, Predrag Klasnja, and Susan Murphy. Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1):1-22, 2020.
[25] Licong Lin, Koulik Khamaru, and Martin J Wainwright. Semi-parametric inference based on adaptively collected data. arXiv preprint arXiv:2303.02534, 2023.
[26] Alexander R Luedtke and Mark J Van Der Laan. Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. Annals of statistics, 44(2):713, 2016.
[27] Seth Neel and Aaron Roth. Mitigating bias in adaptive data gathering via differential privacy. In International Conference on Machine Learning, pages 3720-3729. PMLR, 2018.
[28] Xinkun Nie, Xiaoying Tian, Jonathan Taylor, and James Zou. Why adaptively collected data have negative bias and how to correct for it. In International Conference on Artificial Intelligence and Statistics, pages 1261-1269. PMLR, 2018.
[29] Jaehyeok Shin, Aaditya Ramdas, and Alessandro Rinaldo. Are sample means in multi-armed bandits positively or negatively biased? Advances in Neural Information Processing Systems, 32, 2019.
[30] Jaehyeok Shin, Aaditya Ramdas, and Alessandro Rinaldo. On the bias, risk and consistency of sample means in multi-armed bandits. arXiv preprint arXiv:1902.00746, 2019.
[31] Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, and Deepti Ghadiyaram. Don't judge an object by its context: Learning to overcome contextual bias. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11070-11078, 2020.
[32] Roman Vershynin. High-dimensional probability: An introduction with applications in data science, volume 47. Cambridge university press, 2018.
[33] Martin J Wainwright. High-dimensional statistics: A non-asymptotic viewpoint, volume 48. Cambridge university press, 2019.
[34] Min Xu, Tao Qin, and Tie-Yan Liu. Estimation bias in multi-armed bandit algorithms for search advertising. Advances in Neural Information Processing Systems, 26, 2013.
[35] Elad Yom-Tov, Guy Feraru, Mark Kozdoba, Shie Mannor, Moshe Tennenholtz, and Irit Hochberg. Encouraging physical activity in patients with diabetes: intervention using a reinforcement learning system. Journal of medical Internet research, 19(10):e338, 2017.
[36] Ruohan Zhan, Vitor Hadad, David A Hirshberg, and Susan Athey. Off-policy evaluation via adaptive weighting with data from contextual bandits. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 2125-2135, 2021.
[37] Ruohan Zhan, Zhimei Ren, Susan Athey, and Zhengyuan Zhou. Policy learning with adaptively collected data. Management Science, 2023.
[38] Kelly Zhang, Lucas Janson, and Susan Murphy. Inference for batched bandits. Advances in neural information processing systems, 33:9818-9829, 2020.