LSE creators

Number of items: 65.
Article
  • Hu, Liyuan, Wang, Jitao, Wu, Zhenke, Shi, Chengchun (2025). Generalized fitted Q-iteration with clustered data. Stat, 14(4). https://doi.org/10.1002/sta4.70112 picture_as_pdf
  • Lawrence, Daryl, Avraham, Guy, Yao, Jiaang, Li, Lexin, Shi, Chengchun, Starr, Philip A, Little, Simon J (2025). Cortico-basal oscillations index naturalistic movements during deep brain stimulation. Brain, https://doi.org/10.1093/brain/awaf466 picture_as_pdf
  • Wang, Jiayi, Qi, Zhengling, Shi, Chengchun (2025). Blessing from human-AI interaction: super policy learning in confounded environments. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2025.2574706 picture_as_pdf
  • Wang, Weichen, Shi, Chengchun (2025). From authors to reviewers: leveraging rankings to improve peer review. Journal of the American Statistical Association, picture_as_pdf
  • Xu, Yang, Shi, Chengchun, Luo, Shikai, Wang, Lan, Song, Rui (2025). Doubly robust uncertainty quantification for quantile treatment effects in sequential decision making. Transactions on Machine Learning Research, picture_as_pdf
  • Li, Mengbing, Shi, Chengchun, Wu, Zhenke, Fryzlewicz, Piotr (2025). Testing stationarity and change point detection in reinforcement learning. Annals of Statistics, 53(3), 1230 - 1256. https://doi.org/10.1214/25-aos2501 picture_as_pdf
  • Lin, Xihong, Cai, Tianxi, Donoho, David, Fu, Haoda, Ke, Tracy, Jin, Jiashun, Meng, Xiao-Li, Qu, Annie, Shi, Chengchun & Song, Peter et al (2025). Statistics and AI: a fireside conversation. Harvard Data Science Review, 7(2). https://doi.org/10.1162/99608f92.c066fe9c picture_as_pdf
  • Behnamnia, Armin, Aminian, Gholamali, Aghaei, Alireza, Shi, Chengchun, Tan, Vincent Y. F., R. Rabiee, Hamid (2025). Log-sum-exponential estimator for off-policy evaluation and learning. Proceedings of Machine Learning Research, 267, picture_as_pdf
  • Bian, Zeyu, Shi, Chengchun, Qi, Zhengling, Wang, Lan (2025). Off-policy evaluation in doubly inhomogeneous environments. Journal of the American Statistical Association, 120(550), 1102 - 1114. https://doi.org/10.1080/01621459.2024.2395593 picture_as_pdf
  • Uehara, Masatoshi, Shi, Chengchun, Kallus, Nathan (2025). A review of off-policy evaluation in reinforcement learning. Statistical Science, picture_as_pdf
  • Lan Luo, By, Shi, Chengchun, Wang, Jitao, Wu, Zhenke, Li, Lexin (2025). Multivariate dynamic mediation analysis under a reinforcement learning framework. Annals of Statistics, 53(1), 400 - 425. https://doi.org/10.1214/24-aos2475 picture_as_pdf
  • Shi, Chengchun, Zhou, Yunzhe, Li, Lexin (2024). Testing directed acyclic graph via structural, supervised and generative adversarial learning. Journal of the American Statistical Association, 119(547), 1833 - 1846. https://doi.org/10.1080/01621459.2023.2220169 picture_as_pdf
  • Li, Ting, Shi, Chengchun, Lu, Zhaohua, Li, Yi, Zhu, Hongtu (2024). Evaluating dynamic conditional quantile treatment effects with applications in ridesharing. Journal of the American Statistical Association, 119(547), 1736 - 1750. https://doi.org/10.1080/01621459.2024.2314316 picture_as_pdf
  • Li, Ting, Shi, Chengchun, Wen, Qianglin, Sui, Yang, Qin, Yongli, Lai, Chunbo, Zhu, Hongtu (2024). Combining experimental and historical data for policy evaluation. Proceedings of Machine Learning Research, 235, 28630-28656. picture_as_pdf
  • Luo, Shikai, Yang, Ying, Shi, Chengchun, Yao, Fang, Ye, Jieping, Zhu, Hongtu (2024). Policy evaluation for temporal and/or spatial dependent experiments. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 86(3), 623 - 649. https://doi.org/10.1093/jrsssb/qkad136 picture_as_pdf
  • Zhu, Jin, Wan, Runzhe, Qi, Zhengling, Luo, Shikai, Shi, Chengchun (2024). Robust offline reinforcement learning with heavy-tailed rewards. Proceedings of Machine Learning Research, 238, 541 - 549. picture_as_pdf
  • Li, Jing Jing, Shi, Chengchun, Li, Lexin, Collins, Anne G.E. (2024). Dynamic noise estimation: a generalized method for modeling noise fluctuations in decision-making. Journal of Mathematical Psychology, 119, https://doi.org/10.1016/j.jmp.2024.102842 picture_as_pdf
  • Zhou, Yunzhe, Qi, Zhengling, Shi, Chengchun, Li, Lexin (2023). Optimizing pessimism in dynamic treatment regimes: a Bayesian learning approach. Proceedings of Machine Learning Research, 206, picture_as_pdf
  • Gao, Yuhe, Shi, Chengchun, Song, Rui (2023). Deep spectral Q-learning with application to mobile health. Stat, 12(1). https://doi.org/10.1002/sta4.564 picture_as_pdf
  • Shi, Chengchun, Wan, Runzhe, Song, Ge, Luo, Shikai, Zhu, Hongtu, Song, Rui (2023). A multiagent reinforcement learning framework for off-policy evaluation in two-sided markets. Annals of Applied Statistics, 17(4), 2701 - 2722. https://doi.org/10.1214/22-AOAS1700 picture_as_pdf
  • Zhou, Yunzhe, Shi, Chengchun, Li, Lexin, Yao, Qiwei (2023). Testing for the Markov property in time series via deep conditional generative learning. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85(4), 1204 - 1222. https://doi.org/10.1093/jrsssb/qkad064 picture_as_pdf
  • Wu, Guojun, Song, Ge, Lv, Xiaoxiang, Luo, Shikai, Shi, Chengchun, Zhu, Hongtu (2023). DNet: distributional network for distributional individualized treatment effects. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, 5215 - 5224. https://doi.org/10.1145/3580305.3599809 picture_as_pdf
  • Xu, Yang, Zhu, Jin, Shi, Chengchun, Luo, Shikai, Song, Rui (2023). An instrumental variable approach to confounded off-policy evaluation. Proceedings of Machine Learning Research, 202, 38848 - 38880. picture_as_pdf
  • Shi, Chengchun, Qi, Zhengling, Wang, Jianing, Zhou, Fan (2023). Value enhancement of reinforcement learning via efficient and robust trust region optimization. Journal of the American Statistical Association, 1-15. https://doi.org/10.1080/01621459.2023.2238942 picture_as_pdf
  • Ge, Lin, Wang, Jitao, Shi, Chengchun, Wu, Zhenke, Song, Rui (2023). A reinforcement learning framework for dynamic mediation analysis. Proceedings of Machine Learning Research, 202, 11050 - 11097. picture_as_pdf
  • Wang, Jitao, Shi, Chengchun, Wu, Zhenke (2023). A robust test for the stationarity assumption in sequential decision making. Proceedings of Machine Learning Research, 36355-36379. picture_as_pdf
  • Zhang, Yingying, Shi, Chengchun, Luo, Shikai (2023). Conformal off-policy prediction. Proceedings of Machine Learning Research, 206, 2751-2768. picture_as_pdf
  • Cai, Hengrui, Shi, Chengchun, Song, Rui, Lu, Wenbin (2023). Jump interval-learning for individualized decision making with continuous treatments. Journal of Machine Learning Research, picture_as_pdf
  • Li, Lexin, Shi, Chengchun, Guo, Tengfei, Jagust, William J. (2022). Sequential pathway inference for multimodal neuroimaging analysis. Stat, 11(1). https://doi.org/10.1002/sta4.433 picture_as_pdf
  • Shi, Chengchun, Zhu, Jin, Shen, Ye, Luo, Shikai, Zhu, Hongtu, Song, Rui (2022). Off-policy confidence interval estimation with confounded Markov decision process. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2022.2110878 picture_as_pdf
  • Shi, Chengchun, Li, Lexin (2022). Testing mediation effects using logic of Boolean matrices. Journal of the American Statistical Association, 117(540), 2014 - 2027. https://doi.org/10.1080/01621459.2021.1895177 picture_as_pdf
  • Shi, Chengchun, Luo, Shikai, Le, Yuan, Zhu, Hongtu, Song, Rui (2022). Statistically efficient advantage learning for offline reinforcement learning in infinite horizons. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2022.2106868 picture_as_pdf
  • Shi, Chengchun, Zhang, Shengxing, Lu, Wenbin, Song, Rui (2022). Statistical inference of the value function for reinforcement learning in infinite-horizon settings. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 84(3), 765 - 793. https://doi.org/10.1111/rssb.12465 picture_as_pdf
  • Shi, Chengchun, Uehara, Masatoshi, Uehara, Masatoshi, Huang, Jiawei, Jiang, Nan (2022). A minimax learning approach to off-policy evaluation in confounded Partially Observable Markov Decision Processes. Proceedings of Machine Learning Research, picture_as_pdf
  • Shi, Chengchun, Wang, Xiaoyu, Luo, Shikai, Zhu, Hongtu, Ye, Jieping, Song, Rui (2022). Dynamic causal effects evaluation in A/B testing with a reinforcement learning framework. Journal of the American Statistical Association, 1 - 13. https://doi.org/10.1080/01621459.2022.2027776 picture_as_pdf
  • Shi, Chengchun, Xu, Tianlin, Bergsma, Wicher, Li, Lexin (2021). Double generative adversarial networks for conditional independence testing. Journal of Machine Learning Research, picture_as_pdf
  • Shi, Chengchun, Luo, Shikai, Zhu, Hongtu, Song, Rui (2021). An online sequential test for qualitative treatment effects. Journal of Machine Learning Research, 22, picture_as_pdf
  • Shi, Chengchun, Song, R, Lu, W (2021). Concordance and value information criteria for optimal treatment decision. Annals of Statistics, 49(1), 49 - 75. https://doi.org/10.1214/19-AOS1908 picture_as_pdf
  • Shi, Chengchun, Song, Rui, Lu, Wenbin, Li, Runzi (2020). Statistical inference for high-dimensional models via recursive online-score estimation. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2019.1710154 picture_as_pdf
  • Shi, Chengchun, Lu, Wenbin, Song, Rui (2020). Breaking the curse of nonregularity with subagging: inference of the mean outcome under optimal treatment regimes. Journal of Machine Learning Research, 21, picture_as_pdf
  • Shi, Chengchun, Song, Rui, Chen, Zhao, Li, Runze (2019). Linear hypothesis testing for high dimensional generalized linear models. Annals of Statistics, 47(5), 2671 - 2703. https://doi.org/10.1214/18-AOS1761 picture_as_pdf
  • Shi, Chengchun, Song, Rui, Lu, Wenbin (2019). On testing conditional qualitative treatment effects. Annals of Statistics, 47(4), 2348 - 2377. https://doi.org/10.1214/18-AOS1750 picture_as_pdf
  • Shi, Chengchun, Lu, Wenbin, Song, Rui (2019). A sparse random projection-based test for overall qualitative treatment effects. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2019.1604368 picture_as_pdf
  • Shi, Chengchun, Lu, Wenbin, Song, Rui (2019). Determining the number of latent factors in statistical multi-relational learning. Journal of Machine Learning Research, 20, 1 - 38. picture_as_pdf
  • Shi, Chengchun, Lu, Wenbin, Song, Rui (2018). A massive data framework for M-estimators with cubic-rate. Journal of the American Statistical Association, 113(524), 1698 - 1709. https://doi.org/10.1080/01621459.2017.1360779 picture_as_pdf
  • Shi, Chengchun, Song, Rui, Lu, Wenbin, Fu, Bo (2018). Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 80(4), 681 - 702. https://doi.org/10.1111/rssb.12273 picture_as_pdf
  • Shi, Chengchun, Fan, Ailin, Song, Rui, Lu, Wenbin (2018). High-dimensional A-learning for optimal dynamic treatment regimes. Annals of Statistics, 46(3), 925 - 957. https://doi.org/10.1214/17-AOS1570 picture_as_pdf
  • Shi, Chengchun, Song, Rui, Lu, Wenbin (2016). Robust learning for optimal treatment decision with NP-dimensionality. Electronic Journal of Statistics, 10(2), 2894 - 2921. https://doi.org/10.1214/16-EJS1178 picture_as_pdf
  • Zhang, Peng, Qiu, Zhenguo, Shi, Chengchun (2016). simplexreg: an R package for regression analysis of proportional data using the simplex distribution. Journal of Statistical Software, 71(11). https://doi.org/10.18637/jss.v071.i11 picture_as_pdf
    • Chapter
  • Zhu, Jin, Li, Jingyi, Zhou, Hongyi, Lin, Yinan, Lin, Zhenhua, Shi, Chengchun (2025). Balancing interference and correlation in spatial experimental designs: a causal graph cut approach. In Proceedings of the 42nd International Conference on Machine Learning . ACM Press. picture_as_pdf
  • Zhou, Hongyi, Hanna, Josiah P., Zhu, Jin, Yang, Ying, Shi, Chengchun (2025). Demystifying the paradox of importance sampling with an estimated history-dependent behavior policy in off-policy evaluation. In Proceedings of the 42nd International Conference on Machine Learning . ACM Press. picture_as_pdf
  • Wen, Qianglin, Shi, Chengchun, Yang, Ying, Tang, Niansheng, Zhu, Hongtu (2025). Unraveling the interplay between carryover effects and reward autocorrelations in switchback experiments. In Proceedings of the 42nd International Conference on Machine Learning . ACM Press. picture_as_pdf
  • Uehara, Masatoshi, Kiyohara, Haruka, Bennett, Andrew, Chernozhukov, Victor, Jiang, Nan, Kallus, Nathan, Shi, Chengchun, Sun, Wenguang (2023). Future-dependent value-based off-policy evaluation in POMDPs. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (Eds.), Advances in Neural Information Processing Systems 36 (NeurIPS 2023) . Neural Information Processing Systems Foundation. picture_as_pdf
  • Li, Ting, Shi, Chengchun, Wang, Jianing, Zhou, Fan, Zhu, Hongtu (2023). Optimal treatment allocation for efficient policy evaluation in sequential decision making. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (Eds.), Advances in Neural Information Processing Systems 36 (NeurIPS 2023) . Neural Information Processing Systems Foundation. picture_as_pdf
  • Cai, Hengrui, Shi, Chengchun, Song, Rui, Lu, Wenbin (2021). Deep jump learning for off-policy evaluation in continuous treatment settings. In Proceedings of the 35th Conference on Neural Information Processing Systems . picture_as_pdf
    • Conference or Workshop Item
  • Zhou, Hongyi, Zhu, Jin, Su, Pingfan, Ye, Kai, Yang, Ying, Gavioli Akilagun, Shakeel, Shi, Chengchun (2025-11-30 - 2025-12-07) AdaDetectGPT: adaptive detection of LLM-generated text with statistical guarantees [Paper]. 39th Conference on Neural Information Processing Systems. picture_as_pdf
  • Feng, Jianqi, Shi, Chengchun, Wu, Zhenke, Yan, Xiaodong, Zhao, Wei (2025-11-30 - 2025-12-07) Beyond average value function in precision medicine: maximum probability-driven reinforcement learning for survival analysis [Paper]. 39th Conference on Neural Information Processing Systems. picture_as_pdf
  • Xu, Erhan, Ye, Kai, Zhou, Hongyi, Zhu, Luhan, Quinzan, Francesco, Shi, Chengchun (2025-11-30 - 2025-12-07) Doubly robust alignment for large language models [Paper]. 39th Conference on Neural Information Processing Systems. picture_as_pdf
  • Wu, Xiangkun, Li, Ting, Aminian, Gholamali, Behnamnia, Armin, R. Rabiee, Hamid, Shi, Chengchun (2025-11-30 - 2025-12-07) Pessimistic data integration for policy evaluation [Paper]. 39th Conference on Neural Information Processing Systems. picture_as_pdf
  • Yu, Shuguang, Fang, Shuxing, Peng, Ruixin, Qi, Zhengling, Zhou, Fan, Shi, Chengchun (2024-12-10 - 2024-12-15) Two-way deconfounder for off-policy evaluation in causal reinforcement learning [Paper]. 38th Annual Conference on Neural Information Processing Systems, Vancouver Convention Center, Vancouver, Canada, CAN. picture_as_pdf
  • Li, Jing-Jing, Shi, Chengchun, Li, Lexin, Collins, Anne G.E. (2023-07-26 - 2023-07-29) A generalized method for dynamic noise inference in modeling sequential decision-making [Paper]. Cognition in context, International Convention Centre Sydney, Sydney, Australia, AUS.
  • Wan, Runzhe, Zhang, Sheng, Shi, Chengchun, Luo, Shikai, Song, Rui (2021-08-19 - 2021-08-26) Pattern transfer learning for reinforcement learning in order dispatching [Paper]. International Joint Conference on Artificial Intelligence. picture_as_pdf
  • Shi, Chengchun, Wan, Runzhe, Chernozhukov, Victor, Song, Rui (2021-07-18 - 2021-07-24) Deeply-debiased off-policy interval estimation [Paper]. International Conference on Machine Learning, Online. picture_as_pdf
  • Shi, Chengchun, Wan, Runzhe, Song, Rui, Lu, Wenbin, Leng, Ling (2020-07-12 - 2020-07-18) Does the Markov decision process fit the data: testing for the Markov property in sequential decision making [Paper]. International Conference on Machine Learning, Online. picture_as_pdf
    • Report
  • Hao, Meiling, Su, Pingfan, Hu, Liyuan, Szabo, Zoltan, Zhao, Qianyu, Shi, Chengchun (2024). Forward and backward state abstractions for off-policy evaluation. arXiv. picture_as_pdf