| 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 | 256 | 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 | 230 | 2020 |
| Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage M Uehara, W Sun International Conference on Learning Representations, 2022 | 186 | 2022 |
| Representation Learning for Online and Offline RL in Low-rank MDPs M Uehara, X Zhang, W Sun International Conference on Learning Representations, 2022 | 181 | 2022 |
| 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 | 131 | 2021 |
| Efficiently breaking the curse of horizon in off-policy evaluation with double reinforcement learning N Kallus, M Uehara Operations Research 70 (6), 3282-3302, 2022 | 127* | 2022 |
| A review of off-policy evaluation in reinforcement learning M Uehara, C Shi, N Kallus arXiv preprint arXiv:2212.06355, 2022 | 126 | 2022 |
| 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 | 116 | 2016 |
| Causal inference under unmeasured confounding with negative controls: A minimax learning approach N Kallus, X Mao, M Uehara arXiv preprint arXiv:2103.14029, 2021 | 91 | 2021 |
| 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 | 83 | 2022 |
| 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 | 76 | 2021 |
| Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning N Kallus, M Uehara Advances in Neural Information Processing Systems 32, 2019 | 64 | 2019 |
| Fine-tuning of continuous-time diffusion models as entropy-regularized control M Uehara, Y Zhao, K Black, E Hajiramezanali, G Scalia, NL Diamant, ... arXiv preprint arXiv:2402.15194, 2024 | 63 | 2024 |
| Provable offline preference-based reinforcement learning W Zhan, M Uehara, N Kallus, JD Lee, W Sun arXiv preprint arXiv:2305.14816, 2023 | 57 | 2023 |
| PAC Reinforcement Learning for Predictive State Representations W Zhan, M Uehara, W Sun, JD Lee International Conference on Learning Representations, 2023 | 57 | 2023 |
| 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 | 57 | 2022 |
| Statistically efficient off-policy policy gradients N Kallus, M Uehara Proceedings of the 37th International Conference on Machine Learning, 5089-5100, 2020 | 57 | 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 | 55* | 2020 |
| Localized debiased machine learning: Efficient inference on quantile treatment effects and beyond N Kallus, X Mao, M Uehara Journal of Machine Learning Research 25 (16), 1-59, 2024 | 50* | 2024 |
| Provably efficient reinforcement learning in partially observable dynamical systems M Uehara, A Sekhari, JD Lee, N Kallus, W Sun Advances in Neural Information Processing Systems 35, 578-592, 2022 | 49 | 2022 |
Nathan KallusCornell UniversityVerified email at cornell.edu
