Search Results for author: Zhenke Wu

Found 8 papers, 4 papers with code

A Reinforcement Learning Framework for Dynamic Mediation Analysis

1 code implementation31 Jan 2023 Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song

However, there are a number of applications (e. g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest.

reinforcement-learning Reinforcement Learning (RL)

Doubly Inhomogeneous Reinforcement Learning

1 code implementation8 Nov 2022 Liyuan Hu, Mengbing Li, Chengchun Shi, Zhenke Wu, Piotr Fryzlewicz

Moreover, by borrowing information over time and population, it allows us to detect weaker signals and has better convergence properties when compared to applying the clustering algorithm per time or the change point detection algorithm per subject.

Change Point Detection Clustering +3

Testing Stationarity and Change Point Detection in Reinforcement Learning

1 code implementation3 Mar 2022 Mengbing Li, Chengchun Shi, Zhenke Wu, Piotr Fryzlewicz

Based on the proposed test, we further develop a sequential change point detection method that can be naturally coupled with existing state-of-the-art RL methods for policy optimization in nonstationary environments.

Change Point Detection reinforcement-learning +1

Kernel Deformed Exponential Families for Sparse Continuous Attention

no code implementations1 Nov 2021 Alexander Moreno, Supriya Nagesh, Zhenke Wu, Walter Dempsey, James M. Rehg

Theoretically, we show new existence results for both kernel exponential and deformed exponential families, and that the deformed case has similar approximation capabilities to kernel exponential families.

Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity

2 code implementations2 Feb 2021 Jieru Shi, Zhenke Wu, Walter Dempsey

The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points.


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