متابعة
Masatoshi Uehara
Masatoshi Uehara
Genentech
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عنوان
عدد مرات الاقتباسات
عدد مرات الاقتباسات
السنة
Double reinforcement learning for efficient off-policy evaluation in markov decision processes‏
N Kallus, M Uehara‏
Journal of Machine Learning Research 21 (167), 1-63, 2020‏
2242020
Minimax weight and q-function learning for off-policy evaluation‏
M Uehara, J Huang, N Jiang‏
International Conference on Machine Learning, 9659-9668, 2020‏
2032020
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage‏
M Uehara, W Sun‏
International Conference on Learning Representations, 2022‏
1672022
Representation Learning for Online and Offline RL in Low-rank MDPs‏
M Uehara, X Zhang, W Sun‏
International Conference on Learning Representations, 2022‏
1572022
Efficiently breaking the curse of horizon in off-policy evaluation with double reinforcement learning‏
N Kallus, M Uehara‏
Operations Research 70 (6), 3282-3302, 2022‏
108*2022
Generative adversarial nets from a density ratio estimation perspective‏
M Uehara, I Sato, M Suzuki, K Nakayama, Y Matsuo‏
arXiv preprint arXiv:1610.02920, 2016‏
1082016
Mitigating covariate shift in imitation learning via offline data with partial coverage‏
J Chang, M Uehara, D Sreenivas, R Kidambi, W Sun‏
Advances in Neural Information Processing Systems 34, 965-979, 2021‏
1042021
Efficient reinforcement learning in block mdps: A model-free representation learning approach‏
X Zhang, Y Song, M Uehara, M Wang, A Agarwal, W Sun‏
International Conference on Machine Learning, 26517-26547, 2022‏
732022
Causal inference under unmeasured confounding with negative controls: A minimax learning approach‏
N Kallus, X Mao, M Uehara‏
arXiv preprint arXiv:2103.14029, 2021‏
712021
A review of off-policy evaluation in reinforcement learning‏
M Uehara, C Shi, N Kallus‏
arXiv preprint arXiv:2212.06355, 2022‏
702022
Finite sample analysis of minimax offline reinforcement learning: Completeness, fast rates and first-order efficiency‏
M Uehara, M Imaizumi, N Jiang, N Kallus, W Sun, T Xie‏
arXiv preprint arXiv:2102.02981, 2021‏
682021
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning‏
N Kallus, M Uehara‏
Advances in Neural Information Processing Systems 32, 2019‏
572019
Off-policy evaluation and learning for external validity under a covariate shift‏
M Uehara, M Kato, S Yasui‏
Advances in Neural Information Processing Systems 33, 49-61, 2020‏
51*2020
Statistically efficient off-policy policy gradients‏
N Kallus, M Uehara‏
Proceedings of the 37th International Conference on Machine Learning, 5089-5100, 2020‏
482020
PAC Reinforcement Learning for Predictive State Representations‏
W Zhan, M Uehara, W Sun, JD Lee‏
International Conference on Learning Representations, 2023‏
462023
Localized debiased machine learning: Efficient inference on quantile treatment effects and beyond‏
N Kallus, X Mao, M Uehara‏
Journal of Machine Learning Research 25 (16), 1-59, 2024‏
40*2024
A minimax learning approach to off-policy evaluation in confounded partially observable markov decision processes‏
C Shi, M Uehara, J Huang, N Jiang‏
International Conference on Machine Learning, 20057-20094, 2022‏
402022
Provably efficient reinforcement learning in partially observable dynamical systems‏
M Uehara, A Sekhari, JD Lee, N Kallus, W Sun‏
Advances in Neural Information Processing Systems 35, 578-592, 2022‏
382022
Optimal off-policy evaluation from multiple logging policies‏
N Kallus, Y Saito, M Uehara‏
International Conference on Machine Learning, 5247-5256, 2021‏
342021
Provable offline reinforcement learning with human feedback‏
W Zhan, M Uehara, N Kallus, JD Lee, W Sun‏
ICML 2023 Workshop The Many Facets of Preference-Based Learning, 2023‏
322023
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مقالات 1–20