Seuraa
Chengchun Shi
Chengchun Shi
London School of Economics and Political Science
Vahvistettu sähköpostiosoite verkkotunnuksessa lse.ac.uk - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
High-dimensional A-learning for optimal dynamic treatment regimes
C Shi, A Fan, R Song, W Lu
Annals of statistics 46 (3), 925, 2018
1272018
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
1092022
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
832018
A review of off-policy evaluation in reinforcement learning
M Uehara, C Shi, N Kallus
arXiv preprint arXiv:2212.06355, 2022
782022
Linear hypothesis testing for high dimensional generalized linear models
C Shi, R Song, Z Chen, R Li
Annals of statistics 47 (5), 2671, 2019
632019
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
592021
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
552023
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
512016
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
492020
Off-policy confidence interval estimation with confounded markov decision process
C Shi, J Zhu, Y Shen, S Luo, H Zhu, R Song
Journal of the American Statistical Association 119 (545), 273-284, 2024
482024
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
442022
Deeply-debiased off-policy interval estimation
C Shi, R Wan, V Chernozhukov, R Song
International conference on machine learning, 9580-9591, 2021
392021
Robust learning for optimal treatment decision with np-dimensionality
C Shi, R Song, W Lu
Electronic journal of statistics 10, 2894, 2016
392016
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
362018
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
302020
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
282021
Testing mediation effects using logic of boolean matrices
C Shi, L Li
Journal of the American Statistical Association 117 (540), 2014-2027, 2022
222022
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, 15991-16008, 2023
212023
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
21*2021
An instrumental variable approach to confounded off-policy evaluation
Y Xu, J Zhu, C Shi, S Luo, R Song
International Conference on Machine Learning, 38848-38880, 2023
192023
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Artikkelit 1–20