[17] Botao Hao, Tor Lattimore, and Mengdi Wang. High-dimensional sparse linear bandits. Advances in Neural Information Processing Systems, 33:10753-10763, 2020.
[18] Roger A Horn and Charles R Johnson. Matrix analysis. Cambridge university press, 2012.
[19] Adel Javanmard and Andrea Montanari. Confidence intervals and hypothesis testing for high-dimensional regression. The Journal of Machine Learning Research, 15(1):2869-2909, 2014.
[20] Koulik Khamaru, Yash Deshpande, Lester Mackey, and Martin J Wainwright. Near-optimal inference in adaptive linear regression. arXiv preprint arXiv:2107.02266, 2021.
[21] T_L_Lai and Herbert Robbins. Strong consistency of least-squares estimates in regression models. Proceedings of the National Academy of Sciences, 74(7):2667-2669, 1977.
[22] T L Lai, Herbert Robbins, and C Zi Wei. Strong consistency of least squares estimates in multiple regression ii. Journal of multivariate analysis, 9(3):343-361, 1979.
[23] Tze Leung Lai. Asymptotic properties of nonlinear least squares estimates in stochastic regression models. The Annals of Statistics, pages 1917-1930, 1994.
[24] Tze Leung Lai and Ching Zong Wei. Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems. The Annals of Statistics, 10(1):154-166, 1982.
[25] 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.
[26] 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.
[27] Tor Lattimore and Csaba Szepesvári. Bandit algorithms. Cambridge University Press, 2020.
[28] Lihua Lei, Peter J Bickel, and Noureddine El Karoui. Asymptotics for high dimensional regression m-estimates: fixed design results. Probability Theory and Related Fields, 172:983–1079, 2018.
[29] 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.
[30] Licong Lin, Koulik Khamaru, and Martin J Wainwright. Semi-parametric inference based on adaptively collected data. arXiv preprint arXiv:2303.02534, 2023.
[31] Xinkun Nie, Xiaoying Tian, Jonathan Taylor, and James Zou. Why adaptively collected data have negative bias and how to correct for it. Advances in Neural Information Processing Systems, 84:1261-1269, 2018.
[32] James M Robins. Correcting for non-compliance in randomized trials using structural nested mean models. Communications in Statistics-Theory and methods, 23(8):2379-2412, 1994.
[33] Peter M Robinson. Root-n-consistent semiparametric regression. *Econometrica: Journal of the Econometric Society*, pages 931–954, 1988.
[34] Jaehyeok Shin, Aaditya Ramdas, and Alessandro Rinaldo. On the bias, risk, and consistency of sample means in multi-armed bandits. SIAM Journal on Mathematics of Data Science, 3(4):1278-1300, 2021.
[35] Anastasios A Tsiatis. Semiparametric theory and missing data. 2006.
[36] Mark J Van der Laan, Sherri Rose, et al. Targeted learning: causal inference for observational and experimental data, volume 10. Springer, 2011.
[37] Martin J Wainwright. High-dimensional statistics: A non-asymptotic viewpoint, volume 48. Cambridge University Press, 2019.