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
582016
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
492020
Minimax weight and q-function learning for off-policy evaluation
M Uehara, J Huang, N Jiang
International Conference on Machine Learning, 9659-9668, 2020
422020
Efficiently breaking the curse of horizon: Double reinforcement learning in infinite-horizon processes
N Kallus, M Uehara
arXiv preprint arXiv:1909.05850, 2019
37*2019
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning
N Kallus, M Uehara
Advances in Neural Information Processing Systems 32, 2019
262019
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
6*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, 2020
6*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
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond
N Kallus, X Mao, M Uehara
arXiv preprint arXiv:1912.12945, 2019
4*2019
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
32021
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
N Kallus, M Uehara
Advances in Neural Information Processing Systems 33, 2020
32020
Double reinforcement learning for efficient and robust off-policy evaluation
N Kallus, M Uehara
International Conference on Machine Learning, 5078-5088, 2020
22020
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
N Kallus, X Mao, M Uehara
arXiv preprint arXiv:2103.14029, 2021
12021
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning
N Kallus, M Uehara
arXiv preprint arXiv:2006.03886, 2020
12020
Semiparametric response model with nonignorable nonresponse
M Uehara, JK Kim
arXiv preprint arXiv:1810.12519, 2018
12018
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
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