154 citations · 273 across the 21 of their papers we have counts for
8 papers · 1 filter
Active Measuring in Reinforcement Learning With Delayed Negative Effects
Daiqi Gao, Ziping Xu, Aseel Rawashdeh +2
Measuring states in reinforcement learning (RL) can be costly in real-world settings and may negatively influence future outcomes. We introduce the Actively Observable Markov Decis…
Harnessing Causality in Reinforcement Learning With Bagged Decision Times
Daiqi Gao, Hsin-Yu Lai, Predrag Klasnja +1
We consider reinforcement learning (RL) for a class of problems with bagged decision times. A bag contains a finite sequence of consecutive decision times. The transition dynamics…
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
Karine Karine, Predrag Klasnja, Susan A. Murphy +1
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right typ…
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…
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…
Personalizing Intervention Probabilities By Pooling
Sabina Tomkins, Predrag Klasnja, Susan Murphy
In many mobile health interventions, treatments should only be delivered in a particular context, for example when a user is currently stressed, walking or sedentary. Even in an op…