Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos. Minimax regret bounds for reinforcement learning. In Proceedings of the International Conference on Machine Learning, pages 263-272. PMLR, 2017.
James L Bander and Chelsea C White III. Markov decision processes with noise-corrupted and delayed state observations. Journal of the Operational Research Society, 50(6):660-668, 1999.
Dimitri Bertsekas. Dynamic Programming and Optimal Control: Volume I, volume 1. Athena Scientific, 2012.
Djallel Boueaffouf, Sohini Upadhyay, and Yasaman Khazaeni. Contextual bandit with missing rewards. arXiv preprint arXiv:2007.06368, 2020.
Ronen I Brafman and Moshe Tennenholtz. R-max-a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research, 3(Oct):213-231, 2002.
DM Brooks and Cornelius T Leondes. Markov decision processes with state-information lag. Operations Research, 20(4):904-907, 1972.
Fan Chen, Yu Bai, and Song Mei. Partially observable RL with B-stability: Unified structural condition and sharp sample-efficient algorithms. arXiv preprint arXiv:2209.14990, 2022.
Fan Chen, Huan Wang, Caiming Xiong, Song Mei, and Yu Bai. Lower bounds for learning in revealing POMDPs. arXiv preprint arXiv:2302.01333, 2023.
Christoph Dann and Emma Brunskill. Sample complexity of episodic fixed-horizon reinforcement learning. Advances in Neural Information Processing Systems, 28, 2015.
Christoph Dann, Lihong Li, Wei Wei, and Emma Brunskill. Policy certificates: Towards accountable reinforcement learning. In Proceedings of the International Conference on Machine Learning, pages 1507-1516. PMLR, 2019.
Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, and Michal Valko. Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited. In Algorithmic Learning Theory, pages 578-598. PMLR, 2021.
Miroslav Dudik, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, and Tong Zhang. Efficient optimal learning for contextual bandits. arXiv preprint arXiv:1106.2369, 2011.
Tlamelo Emmanuel, Thabiso Maupong, Dimane Mpoeleng, Thabo Semong, Banyatsang Mphago, and Oteng Tabona. A survey on missing data in machine learning. Journal of Big Data, 8(1):1-37, 2021.
Manegueu Anne Gael, Claire Vernade, Alexandra Carpentier, and Michal Valko. Stochastic bandits with arm-dependent delays. In Proceedings of the International Conference on Machine Learning, pages 3348-3356. PMLR, 2020.
Pedro J García-Laencina, José-Luis Sancho-Gómez, and Aníbal R Figueiras-Vidal. Pattern classification with missing data: a review. Neural Computing and Applications, 19:263-282, 2010.
Benjamin Howson, Ciara Pike-Burke, and Sarah Filippi. Delayed feedback in generalised linear bandits revisited. In International Conference on Artificial Intelligence and Statistics, pages 6095-6119. PMLR, 2023.
Thomas Jaksch, Ronald Ortner, and Peter Auer. Near-optimal regret bounds for reinforcement learning. Journal of Machine Learning Research, 11(51):1563-1600, 2010. URL http://jmlr.org/papers/v11/jaksch10a.html.
Jose M Jerez, Ignacio Molina, Pedro J Garcia-Laencina, Emilio Alba, Nuria Ribelles, Miguel Martin, and Leonardo Franco. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence in Medicine, 50(2):105-115, 2010.
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan. Is Q-learning provably efficient? Advances in Neural Information Processing Systems, 31, 2018.