Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, and John Langford. Learning to search better than your teacher. In International Conference on Machine Learning, pages 2058–2066. PMLR, 2015.
Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, and Liwei Wang. Human-in-the-loop: Provably efficient preference-based reinforcement learning with general function approximation. In International Conference on Machine Learning, pages 3773-3793. PMLR, 2022.
Ching-An Cheng and Byron Boots. Convergence of value aggregation for imitation learning. In International Conference on Artificial Intelligence and Statistics, pages 1801-1809. PMLR, 2018.
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017.
Wei Chu and Zoubin Ghahramani. Preference learning with gaussian processes. In Proceedings of the 22nd international conference on Machine learning, pages 137-144, 2005.
Robert Cohn, Edmund Durfee, and Satinder Singh. Comparing action-query strategies in semi-autonomous agents. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 25, pages 1102-1107, 2011.
Varsha Dani, Thomas P Hayes, and Sham M Kakade. Stochastic linear optimization under bandit feedback. In 21st Annual Conference on Learning Theory, pages 355-366, 2008.
Hal Daumé, John Langford, and Daniel Marcu. Search-based structured prediction. Machine learning, 75:297-325, 2009.
Ofer Dekel, Claudio Gentile, and Karthik Sridharan. Selective sampling and active learning from single and multiple experts. The Journal of Machine Learning Research, 13(1):2655-2697, 2012.
Simon S Du, Yuping Luo, Ruosong Wang, and Hanrui Zhang. Provably efficient q-learning with function approximation via distribution shift error checking oracle. Advances in Neural Information Processing Systems, 32, 2019.
Miroslav Dudík, Katja Hofmann, Robert E Schapire, Aleksandrs Slivkins, and Masrour Zoghi. Contextual dueling bandits. In Conference on Learning Theory, pages 563-587. PMLR, 2015.
Dylan Foster and Alexander Rakhlin. Beyond ucb: Optimal and efficient contextual bandits with regression oracles. In International Conference on Machine Learning, pages 3199-3210. PMLR, 2020.
Dylan Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, and Robert Schapire. Practical contextual bandits with regression oracles. In International Conference on Machine Learning, pages 1539-1548. PMLR, 2018a.
Dylan Foster, Alexander Rakhlin, David Simchi-Levi, and Yunzong Xu. Instance-dependent complexity of contextual bandits and reinforcement learning: A disagreement-based perspective. In Conference on Learning Theory, pages 2059-2059. PMLR, 2021.
Dylan J Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, and Karthik Sridharan. Logistic regression: The importance of being improper. In Conference On Learning Theory, pages 167-208. PMLR, 2018b.
Johannes Furnkranz, Eyke Hüllermeier, Weiwei Cheng, and Sang-Hyeun Park. Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. Machine learning, 89:123-156, 2012.
Aurelien Garivier, Pierre Ménard, and Gilles Stoltz. Explore first, exploit next: The true shape of regret in bandit problems. Mathematics of Operations Research, 44(2):377-399, 2019.
Steve Hanneke and Liu Yang. Minimax analysis of active learning. J. Mach. Learn. Res., 16(1): 3487-3602, 2015.