17 citations · 71 across the 33 of their papers we have counts for
5 papers · 2 filters
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…
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…
Deep Spectral Q-learning with Application to Mobile Health
Yuhe Gao, Chengchun Shi, Rui Song
Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applic…
A Reinforcement Learning Framework for Dynamic Mediation Analysis
Lin Ge, Jitao Wang, Chengchun Shi +2
Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes and receives increasing attention in various scientific domains to el…