Determining the Number of Latent Factors in Statistical Multi-Relational Learning. - Chengchun Shi; Wenbin Lu; Rui Song 006 (J. Mach. Learn. Res., 2019)

Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making. - Chengchun Shi; Runzhe Wan; Rui Song 006; Wenbin Lu; Ling Leng (ICML, 2020)

Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings. - Chengchun Shi; Sheng Zhang 027; Wenbin Lu; Rui Song 006 (CoRR, 2020)

A Reinforcement Learning Framework for Time-Dependent Causal Effects Evaluation in A/B Testing. - Chengchun Shi; Xiaoyu Wang; Shikai Luo; Rui Song 006; Hongtu Zhu; Jieping Ye (CoRR, 2020)

Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space. - Hengrui Cai; Chengchun Shi; Rui Song 006; Wenbin Lu (CoRR, 2020)

Breaking the Curse of Nonregularity with Subagging - Inference of the Mean Outcome under Optimal Treatment Regimes. - Chengchun Shi; Wenbin Lu; Rui Song 006 (J. Mach. Learn. Res., 2020)

Deeply-Debiased Off-Policy Interval Estimation. - Chengchun Shi; Runzhe Wan; Victor Chernozhukov; Rui Song 006 (ICML, 2021)

Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings. - Hengrui Cai; Chengchun Shi; Rui Song 006; Wenbin Lu (NeurIPS, 2021)

Pattern Transfer Learning for Reinforcement Learning in Order Dispatching. - Runzhe Wan; Sheng Zhang 027; Chengchun Shi; Shikai Luo; Rui Song 006 (CoRR, 2021)

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning. - Chengchun Shi; Yunzhe Zhou; Lexin Li (CoRR, 2021)

A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes. - Chengchun Shi; Masatoshi Uehara; Nan Jiang 008 (CoRR, 2021)

Jump Interval-Learning for Individualized Decision Making. - Hengrui Cai; Chengchun Shi; Rui Song 006; Wenbin Lu (CoRR, 2021)

An Online Sequential Test for Qualitative Treatment Effects. - Chengchun Shi; Shikai Luo; Hongtu Zhu; Rui Song 006 (J. Mach. Learn. Res., 2021)

Double Generative Adversarial Networks for Conditional Independence Testing. - Chengchun Shi; Tianlin Xu; Wicher Bergsma; Lexin Li (J. Mach. Learn. Res., 2021)

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes. - Chengchun Shi; Masatoshi Uehara; Jiawei Huang; Nan Jiang 008 (ICML, 2022)

A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets. - Chengchun Shi; Runzhe Wan; Ge Song; Shikai Luo; Rui Song 006; Hongtu Zhu (CoRR, 2022)

Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. - Chengchun Shi; Jin Zhu; Ye Shen; Shikai Luo; Hongtu Zhu; Rui Song 006 (CoRR, 2022)

Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms. - Shikai Luo; Ying Yang; Chengchun Shi; Fang Yao; Jieping Ye; Hongtu Zhu (CoRR, 2022)

Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons. - Chengchun Shi; Shikai Luo; Hongtu Zhu; Rui Song 006 (CoRR, 2022)

Reinforcement Learning in Possibly Nonstationary Environments. - Mengbing Li; Chengchun Shi; Zhenke Wu; Piotr Fryzlewicz (CoRR, 2022)

Blessing from Experts: Super Reinforcement Learning in Confounded Environments. - Jiayi Wang; Zhengling Qi; Chengchun Shi (CoRR, 2022)

Doubly Inhomogeneous Reinforcement Learning. - Liyuan Hu; Mengbing Li; Chengchun Shi; Zhenke Wu; Piotr Fryzlewicz (CoRR, 2022)

A Review of Off-Policy Evaluation in Reinforcement Learning. - Masatoshi Uehara; Chengchun Shi; Nathan Kallus (CoRR, 2022)

Quantile Off-Policy Evaluation via Deep Conditional Generative Learning. - Yang Xu; Chengchun Shi; Shikai Luo; Lan Wang; Rui Song 006 (CoRR, 2022)

Conformal Off-Policy Prediction. - Yingying Zhang; Chengchun Shi; Shikai Luo (AISTATS, 2023)

Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach. - Yunzhe Zhou; Zhengling Qi; Chengchun Shi; Lexin Li (AISTATS, 2023)

A generalized method for dynamic noise inference in modeling sequential decision-making. - Jing-Jing Li; Chengchun Shi; Lexin Li; Anne G. E. Collins (CogSci, 2023)

A Reinforcement Learning Framework for Dynamic Mediation Analysis. - Lin Ge; Jitao Wang; Chengchun Shi; Zhenke Wu; Rui Song 006 (ICML, 2023)

A Robust Test for the Stationarity Assumption in Sequential Decision Making. - Jitao Wang; Chengchun Shi; Zhenke Wu (ICML, 2023)

An Instrumental Variable Approach to Confounded Off-Policy Evaluation. - Yang Xu; Jin Zhu; Chengchun Shi; Shikai Luo; Rui Song 006 (ICML, 2023)

DNet: Distributional Network for Distributional Individualized Treatment Effects. - Guojun Wu; Ge Song; Xiaoxiang Lv; Shikai Luo; Chengchun Shi; Hongtu Zhu (KDD, 2023)

Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making. - Ting Li; Chengchun Shi; Jianing Wang; Fan Zhou; Hongtu Zhu (NeurIPS, 2023)

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. - Masatoshi Uehara; Haruka Kiyohara; Andrew Bennett; Victor Chernozhukov; Nan Jiang 008; Nathan Kallus; Chengchun Shi; Wen Sun 002 (NeurIPS, 2023)

Deep Spectral Q-learning with Application to Mobile Health. - Yuhe Gao; Chengchun Shi; Rui Song 006 (CoRR, 2023)

Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization. - Chengchun Shi; Zhengling Qi; Jianing Wang; Fan Zhou (CoRR, 2023)

Sequential Knockoffs for Variable Selection in Reinforcement Learning. - Tao Ma; Hengrui Cai; Zhengling Qi; Chengchun Shi; Eric B. Laber (CoRR, 2023)

Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing. - Ting Li; Chengchun Shi; Zhaohua Lu; Yi Li 006; Hongtu Zhu (CoRR, 2023)

Testing for the Markov Property in Time Series via Deep Conditional Generative Learning. - Yunzhe Zhou; Chengchun Shi; Lexin Li; Qiwei Yao (CoRR, 2023)

Off-policy Evaluation in Doubly Inhomogeneous Environments. - Zeyu Bian; Chengchun Shi; Zhengling Qi; Lan Wang (CoRR, 2023)

Robust Offline Policy Evaluation and Optimization with Heavy-Tailed Rewards. - Jin Zhu; Runzhe Wan; Zhengling Qi; Shikai Luo; Chengchun Shi (CoRR, 2023)

Jump Interval-Learning for Individualized Decision Making with Continuous Treatments. - Hengrui Cai; Chengchun Shi; Rui Song 006; Wenbin Lu (J. Mach. Learn. Res., 2023)

Robust Offline Reinforcement Learning with Heavy-Tailed Rewards. - Jin Zhu; Runzhe Wan; Zhengling Qi; Shikai Luo; Chengchun Shi (AISTATS, 2024)

Combining Experimental and Historical Data for Policy Evaluation. - Ting Li; Chengchun Shi; Qianglin Wen; Yang Sui; Yongli Qin; Chunbo Lai; Hongtu Zhu (ICML, 2024)

Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning. - Shuguang Yu; Shuxing Fang; Ruixin Peng; Zhengling Qi; Fan Zhou; Chengchun Shi (NeurIPS, 2024)

Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data. - Danyang Wang; Chengchun Shi; Shikai Luo; Will Wei Sun (CoRR, 2024)

Forward and Backward State Abstractions for Off-policy Evaluation. - Meiling Hao; Pingfan Su; Liyuan Hu; Zoltan Szabo; Qingyuan Zhao; Chengchun Shi (CoRR, 2024)

Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences. - Runpeng Dai; Jianing Wang; Fan Zhou; Shikai Luo; Zhiwei Qin; Chengchun Shi; Hongtu Zhu (CoRR, 2024)

Dual Active Learning for Reinforcement Learning from Human Feedback. - Pangpang Liu; Chengchun Shi; Will Wei Sun (CoRR, 2024)

Optimized SVR model for predicting dissolved oxygen levels using wavelet denoising and variable reduction: Taking the Minjiang River estuary as an example. - Peng Zhang; Xinyang Liu; Huiru Zhang; Chengchun Shi; Gangfu Song; Lei Tang; Ruihua Li (Ecol. Informatics, 2025)

Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing. - Jitao Wang; Chengchun Shi; John D. Piette; Joshua R. Loftus; Donglin Zeng; Zhenke Wu (CoRR, 2025)

Statistical Inference in Reinforcement Learning: A Selective Survey. - Chengchun Shi (CoRR, 2025)

Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning. - Kai Ye; Hongyi Zhou; Jin Zhu; Francesco Quinzan; Chengchun Shi (CoRR, 2025)

Deep Distributional Learning with Non-crossing Quantile Network. - Guohao Shen; Runpeng Dai; Guojun Wu; Shikai Luo; Chengchun Shi; Hongtu Zhu (CoRR, 2025)

Semi-pessimistic Reinforcement Learning. - Jin Zhu; Xin Zhou; Jiaang Yao; Gholamali Aminian; Omar Rivasplata; Simon Little; Lexin Li; Chengchun Shi (CoRR, 2025)

Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach. - Zhu Jin; Li Jingyi; Hongyi Zhou; Yinan Lin; Lin Zhenhua; Chengchun Shi (CoRR, 2025)

Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation. - Hongyi Zhou; Josiah P. Hanna; Jin Zhu; Ying Yang; Chengchun Shi (CoRR, 2025)

Doubly Robust Alignment for Large Language Models. - Erhan Xu; Kai Ye; Hongyi Zhou; Luhan Zhu; Francesco Quinzan; Chengchun Shi (CoRR, 2025)

Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning. - Armin Behnamnia; Gholamali Aminian; Alireza Aghaei; Chengchun Shi; Vincent Y. F. Tan; Hamid R. Rabiee (CoRR, 2025)

A Two-armed Bandit Framework for A/B Testing. - Jinjuan Wang; Qianglin Wen; Yu Zhang; Xiaodong Yan; Chengchun Shi (CoRR, 2025)