455 citations · 509 across the 13 of their papers we have counts for
10 papers · 1 filter
Modeling Mobile Health Users as Reinforcement Learning Agents
Eura Shin, Siddharth Swaroop, Weiwei Pan +2
Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e.g. push notifications) tailored to the…
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning
Sarah Rathnam, Susan A. Murphy, Finale Doshi-Velez
In batch reinforcement learning, there can be poorly explored state-action pairs resulting in poorly learned, inaccurate models and poorly performing associated policies. Various r…
Online structural kernel selection for mobile health
Eura Shin, Pedja Klasnja, Susan Murphy +1
Motivated by the need for efficient and personalized learning in mobile health, we investigate the problem of online kernel selection for Gaussian Process regression in the multi-t…
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health
Marianne Menictas, Sabina Tomkins, Susan Murphy
Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning…
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…