Masatoshi Uehara
Title
Cited by
Cited by
Year
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
572016
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
432020
Minimax weight and q-function learning for off-policy evaluation
M Uehara, J Huang, N Jiang
International Conference on Machine Learning, 9659-9668, 2020
352020
Efficiently breaking the curse of horizon: Double reinforcement learning in infinite-horizon processes
N Kallus, M Uehara
arXiv preprint arXiv:1909.05850, 2019
35*2019
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning
N Kallus, M Uehara
arXiv preprint arXiv:1906.03735, 2019
272019
Statistically efficient off-policy policy gradients
N Kallus, M Uehara
Proceedings of the 37th International Conference on Machine Learning, 5089-5100, 2020
112020
Analysis of noise contrastive estimation from the perspective of asymptotic variance
M Uehara, T Matsuda, F Komaki
arXiv preprint arXiv:1808.07983, 2018
72018
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
5*2020
Imputation estimators for unnormalized models with missing data
M Uehara, T Matsuda, JK Kim
International Conference on Artificial Intelligence and Statistics, 831-841, 2020
42020
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, 2020
4*2020
Doubly robust off-policy value and gradient estimation for deterministic policies
N Kallus, M Uehara
arXiv preprint arXiv:2006.03900, 2020
32020
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
N Kallus, X Mao, M Uehara
arXiv preprint arXiv:1912.12945, 2019
3*2019
Double reinforcement learning for efficient and robust off-policy evaluation
N Kallus, M Uehara
International Conference on Machine Learning, 5078-5088, 2020
22020
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning
N Kallus, M Uehara
arXiv preprint arXiv:2006.03886, 2020
12020
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
2021
Fast Rates for the Regret of Offline Reinforcement Learning
Y Hu, N Kallus, M Uehara
arXiv preprint arXiv:2102.00479, 2021
2021
Optimal Off-Policy Evaluation from Multiple Logging Policies
N Kallus, Y Saito, M Uehara
arXiv preprint arXiv:2010.11002, 2020
2020
Information criteria for non-normalized models
T Matsuda, M Uehara, A Hyvarinen
arXiv preprint arXiv:1905.05976, 2019
2019
Semiparametric response model with nonignorable nonresponse
M Uehara, JK Kim
arXiv preprint arXiv:1810.12519, 2018
2018
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift Mark
M Uehara, M Kato, S Yasui
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