2 citations · 2 across the 3 of their papers we have counts for
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cs.LG2023
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
L. L. Ankile, B. S. Ham, K. Mao +4
When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called \emph{user traits}, to inform intervention design. W…
cs.LG2022★ 2 cited
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…
cs.LG2021
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…