154 citations · 231 across the 11 of their papers we have counts for
12 papers
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
Susobhan Ghosh, Raphael Kim, Prasidh Chhabria +5
There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such seq…
Robust Batch Policy Learning in Markov Decision Processes
Zhengling Qi, Peng Liao
We study the offline data-driven sequential decision making problem in the framework of Markov decision process (MDP). In order to enhance the generalizability and adaptivity of th…
IntelligentPooling: Practical Thompson Sampling for mHealth
Sabina Tomkins, Peng Liao, Predrag Klasnja +1
In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinfo…
Batch Policy Learning in Average Reward Markov Decision Processes
Peng Liao, Zhengling Qi, Runzhe Wan +2
We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy tha…
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
Sabina Tomkins, Peng Liao, Predrag Klasnja +2
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of indi…
Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health
Peng Liao, Predrag Klasnja, Susan Murphy
Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, m…
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity
Peng Liao, Kristjan Greenewald, Predrag Klasnja +1
With the recent evolution of mobile health technologies, health scientists are increasingly interested in developing just-in-time adaptive interventions (JITAIs), typically deliver…
Group-driven Reinforcement Learning for Personalized mHealth Intervention
Feiyun Zhu, Jun Guo, Zheng Xu +2
Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health. State-…
Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention
Feiyun Zhu, Peng Liao
Online reinforcement learning (RL) is increasingly popular for the personalized mobile health (mHealth) intervention. It is able to personalize the type and dose of interventions a…
Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention
Feiyun Zhu, Peng Liao, Xinliang Zhu +2
In the wake of the vast population of smart device users worldwide, mobile health (mHealth) technologies are hopeful to generate positive and wide influence on people's health. The…
MRT-SS Calculator: An R Shiny Application for Sample Size Calculation in Micro-Randomized Trials
Nicholas J. Seewald, Ji Sun, Peng Liao
The micro-randomized trial (MRT) is a new experimental design which allows for the investigation of the proximal effects of a "just-in-time" treatment, often provided via a mobile…
Sample Size Calculations for Micro-randomized Trials in mHealth
Peng Liao, Predrag Klasnja, Ambuj Tewari +1
The use and development of mobile interventions are experiencing rapid growth. In "just-in-time" mobile interventions, treatments are provided via a mobile device and they are inte…