[16] T. Fiez, L. Jain, K. G. Jamieson, and L. Ratliff. Sequential experimental design for transductive linear bandits. Advances in neural information processing systems, 32, 2019.
[17] D. Foster and A. Rakhlin. Beyond ucb: Optimal and efficient contextual bandits with regression oracles. In International Conference on Machine Learning, pages 3199-3210. PMLR, 2020.
[18] D. Foster, A. Agarwal, M. Dudik, H. Luo, and R. Schapire. Practical contextual bandits with regression oracles. In International Conference on Machine Learning, pages 1539-1548. PMLR, 2018.
[19] D. A. Freedman. On tail probabilities for martingales. the Annals of Probability, pages 100-118, 1975.
[20] Y. Jedra and A. Proutiere. Optimal best-arm identification in linear bandits. Advances in Neural Information Processing Systems, 33:10007-10017, 2020.
[21] C. Jin, A. Krishnamurthy, M. Simchowitz, and T. Yu. Reward-free exploration for reinforcement learning. In International Conference on Machine Learning, pages 4870-4879. PMLR, 2020.
[22] C. Jin, Q. Liu, and S. Miryoosefi. Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms. Advances in neural information processing systems, 34: 13406-13418, 2021.
[23] K.-S. Jun and C. Zhang. Crush optimism with pessimism: Structured bandits beyond asymptotic optimality. Advances in Neural Information Processing Systems, 33:6366-6376, 2020.
[24] J. Kiefer and J. Wolfowitz. The equivalence of two extremum problems. Canadian Journal of Mathematics, 12:363-366, 1960.
[25] J. Kober, J. A. Bagnell, and J. Peters. Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11):1238-1274, 2013.
[26] J. Langford and T. Zhang. The epoch-greedy algorithm for contextual multi-armed bandits. Advances in neural information processing systems, 20(1):96-1, 2007.
[27] T. Lattimore and R. Munos. Bounded regret for finite-armed structured bandits. Advances in Neural Information Processing Systems, 27, 2014.
[28] T. Lattimore and C. Szepesvári. Bandit algorithms. Cambridge University Press, 2020.
[29] T. Lattimore, C. Szepesvari, and G. Weisz. Learning with good feature representations in bandits and in rl with a generative model. In International Conference on Machine Learning, pages 5662-5670. PMLR, 2020.
[30] J. Lee, A. Pacchiano, V. Muthukumar, W. Kong, and E. Brunskill. Online model selection for reinforcement learning with function approximation. In International Conference on Artificial Intelligence and Statistics, pages 3340-3348. PMLR, 2021.
[31] G. Li, P. Kamath, D. J. Foster, and N. Srebro. Understanding the eluder dimension. Advances in Neural Information Processing Systems, 35:23737-23750, 2022.
[32] J. Li, W. Monroe, A. Ritter, M. Galley, J. Gao, and D. Jurafsky. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541, 2016.
[33] L. Li, W. Chu, J. Langford, and R. 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.
[34] Z. Li, L. Ratliff, K. G. Jamieson, L. Jain, et al. Instance-optimal pac algorithms for contextual bandits. Advances in Neural Information Processing Systems, 35:37590-37603, 2022.
[35] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.