Steve Hanneke and Liu Yang. Toward a general theory of online selective sampling: Trading off mistakes and queries. In International Conference on Artificial Intelligence and Statistics, pages 3997-4005. PMLR, 2021.
Jiafan He, Dongruo Zhou, and Quanquan Gu. Logarithmic regret for reinforcement learning with linear function approximation. In International Conference on Machine Learning, pages 4171-4180. PMLR, 2021.
Baihe Huang, Jason D Lee, Zhaoran Wang, and Zhuoran Yang. Towards general function approximation in zero-sum markov games. In International Conference on Learning Representations, 2022.
Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, and Lin Yang. Randomized exploration in reinforcement learning with general value function approximation. In International Conference on Machine Learning, pages 4607-4616. PMLR, 2021.
Ashesh Jain, Shikhar Sharma, Thorsten Joachims, and Ashutosh Saxena. Learning preferences for manipulation tasks from online coactive feedback. The International Journal of Robotics Research, 34(10):1296-1313, 2015.
Tiancheng Jin and Haipeng Luo. Simultaneously learning stochastic and adversarial episodic mdps with known transition. Advances in neural information processing systems, 33:16557-16566, 2020.
Sham M Kakade and Ambuj Tewari. On the generalization ability of online strongly convex programming algorithms. Advances in Neural Information Processing Systems, 21, 2008.
Junpei Komiyama, Junya Honda, Hisashi Kashima, and Hiroshi Nakagawa. Regret lower bound and optimal algorithm in dueling bandit problem. In Conference on learning theory, pages 1141-1154. PMLR, 2015.
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé III, and John Langford. Active learning for cost-sensitive classification. In International Conference on Machine Learning, pages 1915-1924. PMLR, 2017.
Michael Laskey, Sam Staszak, Wesley Yu-Shu Hsieh, Jeffrey Mahler, Florian T Pokorny, Anca D Dragan, and Ken Goldberg. Shiv: Reducing supervisor burden in dagger using support vectors for efficient learning from demonstrations in high dimensional state spaces. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 462-469. IEEE, 2016.
Tor Lattimore and Csaba Szepesvári. Bandit algorithms. Cambridge University Press, 2020.
Kimin Lee, Laura Smith, Anca Dragan, and Pieter Abbeel. B-pref: Benchmarking preference-based reinforcement learning. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021a.
Kimin Lee, Laura M Smith, and Pieter Abbeel. Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training. In International Conference on Machine Learning, pages 6152-6163. PMLR, 2021b.
Zihao Li, Zhuoran Yang, and Mengdi Wang. Reinforcement learning with human feedback: Learning dynamic choices via pessimism. arXiv preprint arXiv:2305.18438, 2023.
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let's verify step by step. arXiv preprint arXiv:2305.20050, 2023.
Thodoris Lykouris, Max Simchowitz, Alex Slivkins, and Wen Sun. Corruption-robust exploration in episodic reinforcement learning. In Conference on Learning Theory, pages 3242-3245. PMLR, 2021.
Vivek Myers, *Erdem Bıyık*, and Dorsa Sadigh. Active reward learning from online preferences. *arXiv* preprint arXiv:2302.13507, 2023.