| High-dimensional A-learning for optimal dynamic treatment regimes C Shi, A Fan, R Song, W Lu Annals of statistics 46 (3), 925, 2018 | 108 | 2018 |
| Statistical inference of the value function for reinforcement learning in infinite-horizon settings C Shi, S Zhang, W Lu, R Song Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022 | 90 | 2022 |
| A massive data framework for M-estimators with cubic-rate C Shi, W Lu, R Song Journal of the American Statistical Association 113 (524), 1698-1709, 2018 | 74 | 2018 |
| Linear hypothesis testing for high dimensional generalized linear models C Shi, R Song, Z Chen, R Li Annals of statistics 47 (5), 2671, 2019 | 43 | 2019 |
| simplexreg: An R package for regression analysis of proportional data using the simplex distribution P Zhang, Z Qiu, C Shi Journal of Statistical Software 71, 1-21, 2016 | 42 | 2016 |
| Statistical inference for high-dimensional models via recursive online-score estimation C Shi, R Song, W Lu, R Li Journal of the American Statistical Association 116 (535), 1307-1318, 2021 | 41 | 2021 |
| A review of off-policy evaluation in reinforcement learning M Uehara, C Shi, N Kallus arXiv preprint arXiv:2212.06355, 2022 | 39 | 2022 |
| Dynamic causal effects evaluation in a/b testing with a reinforcement learning framework C Shi, X Wang, S Luo, H Zhu, J Ye, R Song Journal of the American Statistical Association 118 (543), 2059-2071, 2023 | 36 | 2023 |
| Deeply-debiased off-policy interval estimation C Shi, R Wan, V Chernozhukov, R Song International conference on machine learning, 9580-9591, 2021 | 34 | 2021 |
| Does the Markov decision process fit the data: Testing for the Markov property in sequential decision making C Shi, R Wan, R Song, W Lu, L Leng International Conference on Machine Learning, 8807-8817, 2020 | 34 | 2020 |
| Robust learning for optimal treatment decision with np-dimensionality C Shi, R Song, W Lu Electronic journal of statistics 10, 2894, 2016 | 34 | 2016 |
| Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects C Shi, R Song, W Lu, B Fu Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2018 | 33 | 2018 |
| Off-policy confidence interval estimation with confounded markov decision process C Shi, J Zhu, S Ye, S Luo, H Zhu, R Song Journal of the American Statistical Association 119 (545), 273-284, 2024 | 31 | 2024 |
| 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 | 31* | 2022 |
| Breaking the Curse of Nonregularity with Subagging---Inference of the Mean Outcome under Optimal Treatment Regimes C Shi, W Lu, R Song Journal of Machine Learning Research 21 (176), 1-67, 2020 | 26 | 2020 |
| Double generative adversarial networks for conditional independence testing C Shi, T Xu, W Bergsma, L Li Journal of Machine Learning Research 22 (285), 1-32, 2021 | 24 | 2021 |
| Testing mediation effects using logic of boolean matrices C Shi, L Li Journal of the American Statistical Association 117 (540), 2014-2027, 2022 | 21 | 2022 |
| Deep jump learning for off-policy evaluation in continuous treatment settings H Cai, C Shi, R Song, W Lu Advances in Neural Information Processing Systems 34, 15285-15300, 2021 | 16* | 2021 |
| Concordance and value information criteria for optimal treatment decision C Shi, R Song, W Lu | 15 | 2021 |
| Future-dependent value-based off-policy evaluation in pomdps M Uehara, H Kiyohara, A Bennett, V Chernozhukov, N Jiang, N Kallus, ... Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
Rui SongNorth Carolina State Universityncsu.edu의 이메일 확인됨
