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
662016
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes.
N Kallus, M Uehara
J. Mach. Learn. Res. 21, 167:1-167:63, 2020
582020
Minimax weight and q-function learning for off-policy evaluation
M Uehara, J Huang, N Jiang
International Conference on Machine Learning, 9659-9668, 2020
532020
Efficiently breaking the curse of horizon: Double reinforcement learning in infinite-horizon processes
N Kallus, M Uehara
arXiv preprint arXiv:1909.05850, 2019
42*2019
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning
N Kallus, M Uehara
Advances in Neural Information Processing Systems 32, 2019
312019
Statistically efficient off-policy policy gradients
N Kallus, M Uehara
Proceedings of the 37th International Conference on Machine Learning, 5089-5100, 2020
132020
Off-policy evaluation and learning for external validity under a covariate shift
M Uehara, M Kato, S Yasui
NeurIPS, 2020
11*2020
Analysis of noise contrastive estimation from the perspective of asymptotic variance
M Uehara, T Matsuda, F Komaki
arXiv preprint arXiv:1808.07983, 2018
82018
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
Localized debiased machine learning: Efficient inference on quantile treatment effects and beyond
N Kallus, X Mao, M Uehara
arXiv preprint arXiv:1912.12945, 2019
5*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
42021
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
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies
N Kallus, M Uehara
Advances in Neural Information Processing Systems 33, 2020
42020
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
N Kallus, X Mao, M Uehara
arXiv preprint arXiv:2103.14029, 2021
32021
Optimal off-policy evaluation from multiple logging policies
N Kallus, Y Saito, M Uehara
International Conference on Machine Learning, 5247-5256, 2021
22021
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
22020
Information criteria for non-normalized models
T Matsuda, M Uehara, A Hyvarinen
arXiv preprint arXiv:1905.05976, 2019
22019
Fast Rates for the Regret of Offline Reinforcement Learning
Y Hu, N Kallus, M Uehara
arXiv preprint arXiv:2102.00479, 2021
12021
Semiparametric response model with nonignorable nonresponse
M Uehara, JK Kim
arXiv preprint arXiv:1810.12519, 2018
12018
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Articles 1–20