Shi, Chengchun

Number of items: 66.
None
  • 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. [In Press]
    • Public
  • 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 [In Press] picture_as_pdf
  • 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. [In Press] 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. [In Press] 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. [In Press] 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
  • 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. [In 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. [In Press] 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, [In 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. [In Press] 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
  • 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
  • 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. [In Press] 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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. [In Press] 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. [In Press] 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, 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. [In Press] 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
    • Restricted
  • Ma, Tao, Zhu, Jin, Cai, Hengrui, Qi, Zhengling, Chen, Yunxiao, Shi, Chengchun, Laber, Eric B. (2026). Sequential knockoffs for variable selection in reinforcement learning. Journal of the American Statistical Association, [In Press] 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 [In Press] picture_as_pdf
  • Wang, Weichen, Shi, Chengchun (2025). From authors to reviewers: leveraging rankings to improve peer review. Journal of the American Statistical Association, [In Press] picture_as_pdf
  • Uehara, Masatoshi, Shi, Chengchun, Kallus, Nathan (2025). A review of off-policy evaluation in reinforcement learning. Statistical Science, [In Press] 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. [In Press] picture_as_pdf