17 citations · 71 across the 33 of their papers we have counts for
8 papers · 1 filter
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
Jin Zhu, Runzhe Wan, Zhengling Qi +2
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applicati…
Off-policy Evaluation in Doubly Inhomogeneous Environments
Zeyu Bian, Chengchun Shi, Zhengling Qi +1
This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions -- temporal stationarity and individual homogeneity are bo…
Testing for the Markov Property in Time Series via Deep Conditional Generative Learning
Yunzhe Zhou, Chengchun Shi, Lexin Li +1
The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of pa…
Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing
Ting Li, Chengchun Shi, Zhaohua Lu +2
Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studie…
Sequential Knockoffs for Variable Selection in Reinforcement Learning
Tao Ma, Jin Zhu, Hengrui Cai +4
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior…
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization
Chengchun Shi, Zhengling Qi, Jianing Wang +1
Reinforcement learning (RL) is a powerful machine learning technique that enables an intelligent agent to learn an optimal policy that maximizes the cumulative rewards in sequentia…