1 code implementation • 31 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.
1 code implementation • 8 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.
no code implementations • 25 Oct 2022 • Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes.
1 code implementation • 3 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.
no code implementations • 1 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.
2 code implementations • 2 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.
Methodology
no code implementations • NeurIPS 2020 • Alexander Moreno, Zhenke Wu, Jamie Yap, David Wetter, Cho Lam, Inbal Nahum-Shani, Walter Dempsey, James M. Rehg
Panel count data describes aggregated counts of recurrent events observed at discrete time points.
no code implementations • 19 Feb 2020 • Seokhyun Chung, Raed Al Kontar, Zhenke Wu
A fundamental assumption is that the output/group membership labels for all observations are known.