| Double reinforcement learning for efficient off-policy evaluation in markov decision processes N Kallus, M Uehara Journal of Machine Learning Research 21 (167), 2020 | 105 | 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 | 99 | 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 | 77 | 2016 |
| Efficiently breaking the curse of horizon in off-policy evaluation with double reinforcement learning N Kallus, M Uehara Operations Research, 2022 | 61* | 2022 |
| Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning N Kallus, M Uehara Advances in Neural Information Processing Systems 32, 2019 | 36 | 2019 |
| Pessimistic model-based offline reinforcement learning under partial coverage M Uehara, W Sun arXiv preprint arXiv:2107.06226, 2021 | 30* | 2021 |
| Representation learning for online and offline rl in low-rank mdps M Uehara, X Zhang, W Sun arXiv preprint arXiv:2110.04652, 2021 | 25 | 2021 |
| 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 | 24* | 2020 |
| Causal inference under unmeasured confounding with negative controls: A minimax learning approach N Kallus, X Mao, M Uehara arXiv preprint arXiv:2103.14029, 2021 | 23 | 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 | 21 | 2021 |
| Statistically efficient off-policy policy gradients N Kallus, M Uehara Proceedings of the 37th International Conference on Machine Learning, 5089-5100, 2020 | 19 | 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 | 15* | 2019 |
| Optimal off-policy evaluation from multiple logging policies N Kallus, Y Saito, M Uehara International Conference on Machine Learning, 5247-5256, 2021 | 12 | 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 | 10 | 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 | 10* | 2020 |
| Analysis of noise contrastive estimation from the perspective of asymptotic variance M Uehara, T Matsuda, F Komaki arXiv preprint arXiv:1808.07983, 2018 | 9 | 2018 |
| Fast rates for the regret of offline reinforcement learning Y Hu, N Kallus, M Uehara arXiv preprint arXiv:2102.00479, 2021 | 7 | 2021 |
| Imputation estimators for unnormalized models with missing data M Uehara, T Matsuda, JK Kim International Conference on Artificial Intelligence and Statistics, 831-841, 2020 | 6 | 2020 |
| Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies N Kallus, M Uehara Advances in Neural Information Processing Systems 33, 2020 | 6 | 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 | 4* | 2022 |
Nathan KallusCornell Universitycornell.edu의 이메일 확인됨
