Chengchun Shi

Please feel free to email me c.shi7@lse.ac.uk if you have any comments.

Some Preprints

* indicates equal contribution

Li J., Wu P. and Shi, C. Counterfactually Safe Reinforcement Learning

Su P*., Ye K*., Gong S., Xu E., Zhu J., Livieri, G. and Shi, C. READER: Reasoning-Enhanced AI-Generated Text Detection

Cen Z*., Zhu J.*, Shen, X. and Shi, C. Perturbation is All You Need for Extrapolating Language Models

Gong, S*., Ye, K*., Zhu, J., Zhang, X., Zhou, H. and Shi, C. Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning.

Liu, P., Shi, C and Sun, W. Reinforcement Learning from Human Feedback: A Statistical Perspective.

Zhou, H*., Ye, K*., Xu, E., Zhu, J., Yang, Y., Gong, S. and Shi, C. Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statistic Demystifying-GRPO

Zhou, H*., Zhu, J*., Yang, Y. and Shi, C. Detecting LLM-Generated Text with Performance Guarantees. StatDetectLLM

Liu, Z., Guo, X., Yang, Z., Lou, F., Zeng, L., Li, M., Qi, Q., Liu, Z., Han, Y., Cheng, D., Feng, X., Wang, H., Shi, C. and Zhang, L. Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning

Wang, J., Wen, Q., Zhang, Y., Yan, X. and Shi, C. A Two-armed Bandit Framework for A/B Testing.

Ye, K*., Zhou, H*., Zhu, J*., Quinzan, F. and Shi, C. Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning VRPO

Shen, G., Dai, R., Wu, G., Luo, S., Shi, C. and Zhu, H. Deep Distributional Learning with Non-crossing Quantile Network

Shi, C. Statistical Inference in Reinforcement Learning: A Selective Survey

Wang, J., Shi, C., Piette, J., Loftus, J., Zeng, D. and Wu, Z. Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing

Liu, P., Shi, C. and Sun, W. Dual Active Learning for Reinforcement Learning from Human Feedback

Hao, M*., Su, P*., Hu, L., Szabó, Z., Zhao, Q. and Shi, C. Off-policy Evaluation with Deeply-abstracted States . state-abstraction

Dai, R*., Wang, J*., Zhou, F*., Luo, S., Qin, Q., Shi, C., and Zhu, H. Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences

Hu, L*., Li, M*., Shi, C., Wu, Z. and Fryzlewicz, P. Doubly Inhomogeneous Reinforcement Learning. DIRL
slides presented at CMStatistics 2022.

Publications/accepted manuscripts

AOAS
Liu, H*., Wang, D., Shi, C, Luo, S. and Sun, W. (2026+). Pessimistic Causal Reinforcement Learning with Applications to Ride-sharing
JMLR
Zhang, J., Wang, J., Shi, C., Piette, J., Zeng, D. and Wu, Z. (2026) PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing.
EMNLP
Gong S*., Xu E*. Ye K*., Livieri, G., Quinzan F. and Shi, C. (2026) BASIS: Batchwise Advantage Estimation from Single-Rollout Information Sharing for LLM Reasoning (findings).
Sinica
Zhao, T., Shi, C., Qi, Z. and Wang, L. (2026+) Sparse Additive Off-Policy Evaluation for Reinforcement Learning with Potentially Limited Number of Trajectories.
JASA
Sun, K., Kong, L., Zhu, H. and Shi, C. (2026) ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments. ARMAdesign
slides
JASA
Zhu, J., Zhou, X., Yao, J., Aminian, G., Rivasplata, O., Little, S., Li, L. and Shi, C. (2026) Semi-pessimistic Reinforcement Learning.
Stat Sci
Uehara, M., Shi, C. and Kallus, N. (2026). A Review of Off-Policy Evaluation in Reinforcement Learning.
STAI-X
Cen Z*., Gu, C*., Zhu, J., Li, T., Chen, Y. and Shi, C.. (2026) Learning Perturbations to Extrapolate Your LLM (honorable mentions).
STAI-X
Li, M*., Zhu, J*., Li, J. and Shi, C.. (2026). Segmenting Human–LLM Co-authored Text via Change Point Detection DetectLLMSegmentation
STAI-X
Yu, S*., Li, T*., Lu, Y. Shi, C., Zhou, F., Zou, Z., Zhen, P. and Zhu, H. (2026). Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data
ICML
Wen, Q*., Wu, X*., Shi, C., Li, T., Tang, N., Zhang, Y. and Zhu, H. (2026). Robust Sequential Experimental Design for A/B Testing
AOAS
Yang, Y., Shi, C, Yao, F., Wang, S. and Zhu, H. (2026+). Spatially Randomized Designs Can Enhance Policy Evaluation
CVPR
Qi, X*., Ye, K*., Shi, C, Yang, Y., Zhou, H. and Zhu, J. (2026). A Difference-in-Difference Approach to Detecting AI-Generated Images
ICLR
Zhou, H*, Zhu, J*., Ye., K., Yang, Y., Xu, E. and Shi, C. (2026). Learn-to-Distance: Distance Learning for Detecting LLM-Generated Text L2D
ICLR
Wu, X*., Wen, Q*., Zhang, Y*., Zhu, H., Li, T. and Shi, C. (2026). Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning
Brain
Lawrence, D., Avraham, G.,Yao J., Li, L., Shi, C., Starr, P.A. and Little, S. (2025) Cortico-basal oscillations index naturalistic movements during deep brain stimulation.
JASA
Ma, T*., Zhu, J*., Cai, H., Qi, Z., Chen, Y., Shi, C. and Laber, E. (2025+) Sequential Knockoffs for Variable Selection in Reinforcement Learning SEEK
STAT
Hu, L., Wang, J., Wu, Z. and Shi, C. (2025) Generalized Fitted Q-Iteration with Clustered Data.
JASA
Wang, W. and Shi, C. (2025+) From Authors to Reviewers: Leveraging Rankings to Improve Peer Review
Discussion of "Analysis of the ICML 2023 Ranking Data: Can Authors’ Opinions of Their Own Papers Assist Peer Review in Machine Learning?"
JASA
Wang, J., Qi, Z. and Shi, C. (2025+) Blessing from Human-AI Interaction: Super Policy Learning in Confounded Environments
NeurIPS
Xu, E*., Ye, K*., Zhou, H*., Zhu, L., Quinzan, F. and Shi, C. (2025). Doubly Robust Alignment for Large Language Models DRPO4LLM
slides video presented at Tsinghua Statistics + AI Frontier Summit
NeurIPS
Zhou, H*., Zhu, J*., Su, P., Ye, K., Yang, Y., Gavioli-Akilagun SA. and Shi, C. (2025). AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees AdaDetectGPT
video presented at 狗熊会
NeurIPS
Wu, X*., Li, T*., Aminian, G., Behnamnia, A., Rabiee, H. and Shi, C. (2025). Pessimistic Data Integration for Policy Evaluation.
NeurIPS
Feng, J., Shi, C., Wu, Z., Yan, X. and Zhao, W. (2025). Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis.
TMLR
Xu, Y., Shi, C., Luo, S., Wang, L. and Song, R. (2025). Doubly Robust Uncertainty Quantification for Quantile Treatment Effects in Sequential Decision Making. 2023 JSM Student Paper Award
ICML
Zhu, J*., Li, J*., Zhou, H., Lin, Y., Lin, Z., Shi, C. (2025). Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach CausalGraphCut
ICML
Wen, Q*., Shi, C*., Yang, Y., Tang, N., Zhu, H. (2025). Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments SwitchMDP
ICML
Zhou, H., Hanna, J., Zhu, J., Yang, Y., Shi, C. (2025). Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation
ICML
Behnamnia, A., Aminian, G., Aghaei, A., Shi, C., Tan, V.Y., Rabiee, H. (2025). Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning (spotlight, top 2.6% of submissions).
HDSR
StatsUpAI Interest Group (2025). Statistics and AI: A Fireside Conversation.
AOS
Li, M., Shi, C., Wu, Z. and Fryzlewicz, P. (2025). Testing Stationarity and Change Point Detection in Reinforcement Learning CUSUM-RL
slides video presented at JSM 2022.
AOS
Luo, L*., Shi, C*., Wang, J*, Wu, Z. and Li, L. (2025). Multivariate Dynamic Mediation Analysis under a Reinforcement Learning Framework MedtimeRL
JASA
Bian, Z., Shi, C., Qi, Z. and Wang, L. (2025). Off-policy Evaluation in Doubly Inhomogeneous Environments 2FEOPE
NeurIPS
Yu, S., Fang, S., Peng, R., Qi, Z., Zhou, F. and Shi, C. (2024). Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning Two-way-deconfounder
ICML
Li, T*., Shi, C*., Wen, Q., Sui, Y., Qin, Y., Lai, C. and Zhu, H. (2024). Combining Experimental and Historical Data for Policy Evaluation Data_Combination
J Math Psychol
Li, J., Shi, C., Li, L. and Collins, A. (2024). Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making dynamic_noise_estimation
JASA
Shi, C*., Qi, Z*., Wang, J. and Zhou, F. (2024). Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization VEPO
JASA
Shi, C., Zhou, Y. and Li, L. (2024). Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning SUGAR
slides presented at JSM 2021
JASA
Li, T*., Shi, C*., Lu, Z., Li, Y. and Zhu, H. (2024). Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing CQSTVCM
JASA
Shi, C., Zhu, J., Shen, Y., Luo, S., Zhu, H. and Song, R. (2024). Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process COPE
JASA
Shi, C., Luo, S., Le, Y., Zhu, H. and Song, R. (2024). Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons SEAL
JRSS-B
Luo, S*., Yang, Y*., Shi, C*., Yao, F., Ye, J. and Zhu, H. (2024). Policy Evaluation for Temporal and/or Spatial Dependent Experiments STVCM
AISTATS
Zhu, J*., Wan, R*., Qi, Z., Luo, S. and Shi, C. (2024). Robust Offline Reinforcement Learning with Heavy-Tailed Rewards ROOM
NeurIPS
Uehara, M., Kiyohara, H., Bennett, A., Chernozhukov, V., Jiang, N., Kallus, N., Shi, C. and Sun, W. (2023) Future-Dependent Value-Based Off-Policy Evaluation in POMDPs (spotlight) future-dependent-ope
NeurIPS
Li, T*., Shi, C*., Wang, J., Zhou, F. and Zhu, H. (2023). Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making MDPdesign
JRSS-B
Zhou, Y., Shi, C., Li, L. and Yao, Q. (2023). Testing for the Markov Property in Time Series via Deep Conditional Generative Learning markov_test
AOAS
Shi, C., Wan, R., Song, G., Luo, S., Zhu, H. and Song, R. (2023). A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets CausalMARL
JASA
Shi, C*., Wang, X*., Luo, S., Zhu, H., Ye, J. and Song, R. (2023). Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework CausalRL
slides video presented at Online Causal Inference Seminar
KDD
Wu, G., Song, G., Lv, X., Luo, S., Shi, C. and Zhu, H. (2023). DNet: Distributional Network for Distributional Individualized Treatment Effects.
ICML
Ge, L., Wang, J., Shi, C., Wu, Z. and Song, R. (2023). A Reinforcement Learning Framework for Dynamic Mediation Analysis MediationRL
2023 ICSA Student Paper Award
ICML
Xu, Y., Zhu, J., Shi, C., Luo, S. and Song, R. (2023). An Instrumental Variable Approach to Confounded Off-Policy Evaluation IVMDP
ICML
Wang, J., Shi, C. and Wu, Z. (2023). A Robust Test for the Stationarity Assumption in Sequential Decision Making. Double-CUSUM-RL
CogSci
Li, J., Shi, C., Li, L. and Collins, A. (2023). Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making.
HDSR
Shi, C. (2023). The Impact of David Cox’s Work and Leadership on My Research.
STAT
Gao, Y., Shi, C. and Song, R. (2023). Deep Spectral Q-learning with Application to Mobile Health. 2022 JSM Student Paper Award
JMLR
Cai, H*., Shi, C*., Song, R. and Lu, W. (2023). Jump Interval-Learning for Individualized Decision Making with Continuous Treatments. JQL
AISTATS
Zhou, Y., Qi, Z., Shi, C. and Li, L. (2023). Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach. PBL
AISTATS
Zhang, Y., Shi, C. and Luo, S. (2023). Conformal Off-Policy Prediction. R code COPP
JASA
Shi, C. and Li, L. (2022). Testing Mediation Effects Using Logic of Boolean Matrices. LOGAN
slides presented at JSM 2021.
JRSS-B
Shi, C., Zhang, S., Song, R. and Lu, W. (2022). Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings. SAVE
slides presented at ICSA 2019.
ICML
Shi, C*., Uehara, M*., Huang, J. and Jiang, N. (2022). A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes (long talk, top 2%). Confounded-POMDP-OPE
video presented at ICML.
STAT
Li, L., Shi, C., Guo, T. and Jagust, W. (2022). Sequential Pathway Inference for Multimodal Neuroimaging Analysis. LOGAN
slides presented at JSM 2021.
JMLR
Shi, C., Xu, T., Bergsma, W. and Li, L. (2021). Double Generative Adversarial Networks for Conditional Independence Testing. dgcit
JMLR
Shi, C., Luo, S., Zhu, H. and Song, R. (2021). An Online Sequential Test for Qualitative Treatment Effects.
NeurIPS
Cai, H*. Shi, C*., Song, R. and Lu, W. (2021). Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings. 2021 ENAR Distinguished Student Paper Awards
DJL
video presented at NeurIPS.
IJCAI workshop
Wan, R*., Zhang, S*., Shi, C., Luo, S. and Song, R. (2021). Pattern Transfer Learning for Reinforcement Learning in Order Dispatching (best paper).
video presented at the workshop.
ICML
Shi, C*., Wan, R*., Chernozhukov, V. and Song, R. (2021). Deeply-Debiased Off-Policy Interval Estimation (long talk, top 3%). D2OPE
video presented at ICML.
JASA
Shi, C., Song, R., Lu, W. and Li. R. (2021). Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation (ROSE). R code for linear/logistic regression
AOS
Shi, C., Song, R. and Lu, W. (2021). Concordance and Value Information Criteria for Optimal Treatment Decision (CIVIC)
JMLR
Shi, C., Lu, W. and Song, R. (2020). Breaking the Curse of Nonregularity with Subagging --- Inference of the Mean Outcome under Optimal Treatment Regimes. R and C sample code subagging2.cpp sb.r
ICML
Shi, C., Wan, R., Song, R., Lu, W. and Leng, L. (2020). Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making. TestMDP
slides video presented at CMStatistics 2020, ICML 2020, JSM 2020 and EYSM 2021.
JASA
Shi, C., Lu, W. and Song, R. (2020). A Sparse Random Projection-based Test for Overall Qualitative Treatment Effects
AOS
Shi, C., Song, R., Chen, Z. and Li, R. (2019). Linear Hypothesis Testing for High Dimensional Generalized Linear Models. 2018 IMS travel award
R code for linear/ logistic/ Poisson regression
AOS
Shi, C., Lu, W., and Song, R. (2019). On Testing Conditional Qualitative Treatment Effects. 2017 IMS travel award
slides presented at JSM 2017
JMLR
Shi, C., Lu, W. and Song, R. (2019). Determining the Number of Latent Factors in Multirelational Learning.
JASA
Shi, C., Lu, W., and Song, R. (2018). A Massive Data Framework for M-estimators with Cubic-Rate.
JRSS-B
Shi, C., Song, R., Lu, W., and Fu, B. (2018). Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects. ITRLearn
slides presented at JSM 2016, poster presented at 2018 NCSU research symposium.
AOS
Shi, C., Fan, A., Song, R., and Lu, W. (2018). High-Dimensional A-Learning for Optimal Dynamic Treatment Regimes. ITRSelect
slides presented at ENAR 2016
JRSS-C
Shi, C., Song, R. and Lu, W. (2018). Discussion of “Optimal Treatment Allocations in Space and Time for On-Line Control of an Emerging Infectious Disease”.
JRSS-B
Shi, C., Song, R. and Lu, W. (2017). Discussion of “Random Projection Ensemble Classification”.
EJS
Shi, C., Song, R. and Lu, W. (2016). Robust Learning for Optimal Treatment Decision with NP-Dimensionality.
J Stat Soft
Zhang, P., Qiu, Z. and Shi, C. (2016). simplexreg: An R Package for Regression Analysis of Proportional Data Using Simplex Distribution. simplexreg