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)

Multi-Objective Reinforcement Learning for Infectious Disease Control with Application to COVID-19 Spread. - Runzhe Wan; Xinyu Zhang; Rui Song 006 (CoRR, 2020)

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

Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control. - Runzhe Wan; Xinyu Zhang; Rui Song 006 (KDD, 2021)

Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models. - Runzhe Wan; Lin Ge; Rui Song 006 (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)

Safe Exploration for Efficient Policy Evaluation and Comparison. - Runzhe Wan; Branislav Kveton; Rui Song 006 (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)

Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies. - Runzhe Wan; Yingying Li; Wenbin Lu; Rui Song 006 (CoRR, 2022)

Heterogeneous Synthetic Learner for Panel Data. - Ye Shen; Runzhe Wan; Hengrui Cai; Rui Song 006 (CoRR, 2022)

Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework. - Runzhe Wan; Lin Ge; Rui Song 006 (AISTATS, 2023)

Multiplier Bootstrap-based Exploration. - Runzhe Wan; Haoyu Wei; Branislav Kveton; Rui Song 006 (ICML, 2023)

Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring. - Runzhe Wan; Yu Liu 080; James McQueen; Doug Hains; Rui Song 006 (KDD, 2023)

STEEL: Singularity-aware Reinforcement Learning. - Xiaohong Chen; Zhengling Qi; Runzhe Wan (CoRR, 2023)

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

Zero-Inflated Bandits. - Haoyu Wei; Runzhe Wan; Lei Shi; Rui Song 006 (CoRR, 2023)

Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches. - Yu Liu 080; Runzhe Wan; James McQueen; Doug Hains; Jinxiang Gu; Rui Song 006 (AAAI, 2024)

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

A Review of Reinforcement Learning in Financial Applications. - Yahui Bai; Yuhe Gao; Runzhe Wan; Sheng Zhang 027; Rui Song 006 (CoRR, 2024)

Know When to Fold: Futility-Aware Early Termination in Online Experiments. - Yu Liu 080; Runzhe Wan; Yian Huang; James McQueen; Doug Hains; Jinxiang Gu; Rui Song 006 (WWW (Companion Volume), 2025)

A Review of Causal Decision Making. - Lin Ge; Hengrui Cai; Runzhe Wan; Yang Xu; Rui Song 006 (CoRR, 2025)