Masatoshi Uehara

Citirano

	Sve	Od 2018.
Citati	1012	999
H-indeks	15	15
i10-indeks	21	21

380

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201620172018201920202021202220235 7 19 24 128 223 380 222

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Nathan KallusCornell UniversityPotvrđena adresa e-pošte na cornell.edu
Wen SunAssistant Professor, Cornell UniversityPotvrđena adresa e-pošte na cornell.edu
Nan JiangAssistant Professor of Computer Science, UIUCPotvrđena adresa e-pošte na illinois.edu
Xiaojie Mao (毛小介)Assistant Professor, School of Economics and Management, Tsinghua UniversityPotvrđena adresa e-pošte na sem.tsinghua.edu.cn
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPotvrđena adresa e-pošte na princeton.edu
Takeru MatsudaUniversity of Tokyo & RIKEN Center for Brain SciencePotvrđena adresa e-pošte na riken.jp
Xuezhou ZhangPrinceton UniversityPotvrđena adresa e-pošte na princeton.edu
Rahul KidambiAmazon SearchPotvrđena adresa e-pošte na uw.edu
Tengyang XieUniversity of Illinois at Urbana-ChampaignPotvrđena adresa e-pošte na illinois.edu
Masaaki ImaizumiThe University of TokyoPotvrđena adresa e-pošte na g.ecc.u-tokyo.ac.jp
Ayush SekhariMITPotvrđena adresa e-pošte na mit.edu
Andrew BennettCornell UniversityPotvrđena adresa e-pošte na cornell.edu
Jae kwang KimLAS Dean's professor, Statistics Department, Iowa State UniversityPotvrđena adresa e-pošte na iastate.edu
Mengdi WangCenter for Statistics & Machine Learning, Electrical and Computer Engineering, PrincetonPotvrđena adresa e-pošte na princeton.edu
Alekh AgarwalGooglePotvrđena adresa e-pošte na google.com
Yuta SaitoCornell UniversityPotvrđena adresa e-pošte na cornell.edu
Yichun HuCornell UniversityPotvrđena adresa e-pošte na cornell.edu
Whitney NeweyProfessor of Economics, MITPotvrđena adresa e-pošte na mit.edu
Vasilis SyrgkanisAssistant Professor, Stanford UniversityPotvrđena adresa e-pošte na stanford.edu
Aapo HyvarinenUniversity of HelsinkiPotvrđena adresa e-pošte na helsinki.fi

Prati

Masatoshi Uehara

Cornell University

Potvrđena adresa e-pošte na cornell.edu - Početna stranica

Reinforcement Learning Causal Inference Machine Learning


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
Double reinforcement learning for efficient off-policy evaluation in markov decision processes N Kallus, M Uehara The Journal of Machine Learning Research 21 (1), 6742-6804, 2020	145	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	140	2020
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	90	2016
Efficiently breaking the curse of horizon in off-policy evaluation with double reinforcement learning N Kallus, M Uehara Operations Research, 2022	84*	2022
Pessimistic model-based offline reinforcement learning under partial coverage M Uehara, W Sun arXiv preprint arXiv:2107.06226, 2021	72*	2021
Representation learning for online and offline rl in low-rank mdps M Uehara, X Zhang, W Sun arXiv preprint arXiv:2110.04652, 2021	59	2021
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning N Kallus, M Uehara Advances in Neural Information Processing Systems 32, 2019	45	2019
Causal inference under unmeasured confounding with negative controls: A minimax learning approach N Kallus, X Mao, M Uehara arXiv preprint arXiv:2103.14029, 2021	43	2021
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	42	2021
Statistically efficient off-policy policy gradients N Kallus, M Uehara Proceedings of the 37th International Conference on Machine Learning, 5089-5100, 2020	34	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	31*	2020
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	25*	2022
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	25	2022
Localized debiased machine learning: Efficient inference on quantile treatment effects and beyond N Kallus, X Mao, M Uehara arXiv preprint arXiv:1912.12945, 2019	21*	2019
Optimal off-policy evaluation from multiple logging policies N Kallus, Y Saito, M Uehara International Conference on Machine Learning, 5247-5256, 2021	19	2021
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	15	2021
Fast rates for the regret of offline reinforcement learning Y Hu, N Kallus, M Uehara arXiv preprint arXiv:2102.00479, 2021	15	2021
Mitigating covariate shift in imitation learning via offline data without great coverage JD Chang, M Uehara, D Sreenivas, R Kidambi, W Sun arXiv preprint arXiv:2106.03207, 2021	14	2021
A unified statistically efficient estimation framework for unnormalized models M Uehara, T Kanamori, T Takenouchi, T Matsuda International Conference on Artificial Intelligence and Statistics, 809-819, 2020	13*	2020
Provably efficient reinforcement learning in partially observable dynamical systems M Uehara, A Sekhari, JD Lee, N Kallus, W Sun arXiv preprint arXiv:2206.12020, 2022	12	2022

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