Masatoshi Uehara

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Citations	1875	1841
h-index	22	22
i10-index	35	35

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Nathan KallusCornell UniversityVerified email at cornell.edu
Wen SunAssistant Professor, Cornell UniversityVerified email at cornell.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Xiaojie Mao (毛小介)Assistant Professor, School of Economics and Management, Tsinghua UniversityVerified email at sem.tsinghua.edu.cn
Nan JiangAssociate Professor of Computer Science, UIUCVerified email at illinois.edu
Wenhao ZhanGraduate Student, Princeton UniversityVerified email at princeton.edu
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
Xuezhou ZhangBoston UniversityVerified email at bu.edu
Chengchun ShiLondon School of Economics and Political ScienceVerified email at lse.ac.uk
Tommaso BiancalaniGenentechVerified email at gene.com
Yulai ZhaoPrinceton UniversityVerified email at princeton.edu
Gabriele ScaliaGenentechVerified email at gene.com
Vasilis SyrgkanisAssistant Professor, Stanford UniversityVerified email at stanford.edu
Mengdi WangCenter for Statistics & Machine Learning, ECE, Princeton UniversityVerified email at princeton.edu
Whitney NeweyProfessor of Economics, MITVerified email at mit.edu
Yutaka MatsuoProfessor, University of TokyoVerified email at weblab.t.u-tokyo.ac.jp
Alekh AgarwalGoogleVerified email at google.com
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-MadisonVerified email at cs.wisc.edu
Yichun HuCornell UniversityVerified email at cornell.edu
Victor ChernozhukovProfessor, Department of Economics + Center for Statistics and Data Science, MIT;Verified email at mit.edu

Masatoshi Uehara

Genentech

Verified email at gene.com - Homepage

Machine Learning Reinforcement Learning Generative Models Drug Discovery Causal Inference


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	206	2020
Minimax weight and q-function learning for off-policy evaluation M Uehara, J Huang, N Jiang International Conference on Machine Learning, 9659-9668, 2020	188	2020
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage M Uehara, W Sun International Conference on Learning Representations, 2022	152	2022
Representation Learning for Online and Offline RL in Low-rank MDPs M Uehara, X Zhang, W Sun International Conference on Learning Representations, 2022	137	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	105	2016
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	99*	2022
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	90	2021
Causal inference under unmeasured confounding with negative controls: A minimax learning approach N Kallus, X Mao, M Uehara arXiv preprint arXiv:2103.14029, 2021	65	2021
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	63	2022
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	62	2021
A review of off-policy evaluation in reinforcement learning M Uehara, C Shi, N Kallus arXiv preprint arXiv:2212.06355, 2022	55	2022
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning N Kallus, M Uehara Advances in Neural Information Processing Systems 32, 2019	55	2019
Statistically efficient off-policy policy gradients N Kallus, M Uehara Proceedings of the 37th International Conference on Machine Learning, 5089-5100, 2020	45	2020
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	45*	2020
PAC Reinforcement Learning for Predictive State Representations W Zhan, M Uehara, W Sun, JD Lee International Conference on Learning Representations, 2023	43	2023
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	38	2022
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	35	2022
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	32*	2024
Optimal off-policy evaluation from multiple logging policies N Kallus, Y Saito, M Uehara International Conference on Machine Learning, 5247-5256, 2021	30	2021
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	26	2023

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