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Found 20 papers, 3 papers with code
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally.
Online Uniform Risk Times Sampling: First Approximation Algorithms, Learning Augmentation with Full Confidence Interval Integration
We propose two online approximation algorithms for this problem, one with and one without learning augmentation, and provide rigorous theoretical performance guarantees for them using competitive ratio analysis.
Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification.
Dyadic Reinforcement Learning
This presents opportunities in mobile health to design interventions that target the dyadic relationship -- the relationship between a target person and their care partner -- with the aim of enhancing social support.
Online learning in bandits with predicted context
When the context error is non-vanishing, classical bandit algorithms fail to achieve sublinear regret.
Effect-Invariant Mechanisms for Policy Generalization
A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks.
Contextual Bandits with Budgeted Information Reveal
Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments.
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity.
Modeling Mobile Health Users as Reinforcement Learning Agents
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 user's needs.
Doubly robust nearest neighbors in factor models
We consider a matrix completion problem with missing data, where the $(i, t)$-th entry, when observed, is given by its mean $f(u_i, v_t)$ plus mean-zero noise for an unknown function $f$ and latent factors $u_i$ and $v_t$.
Counterfactual inference for sequential experiments
Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible scale -- mean outcome under different treatments for each unit and each time -- with minimal assumptions on the adaptive treatment policy.
Online structural kernel selection for mobile health
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-task setting.
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health
We propose an algorithm for providing physical activity suggestions in mHealth settings.
IntelligentPooling: Practical Thompson Sampling for mHealth
In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment.
Batch Policy Learning in Average Reward Markov Decision Processes
The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
Power Constrained Bandits
However, when bandits are deployed in the context of a scientific study -- e. g. a clinical trial to test if a mobile health intervention is effective -- the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective.
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
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 individuals.
Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health
The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map an individual's current state (e. g., individual's past behaviors as well as current observations of time, location, social activity, stress and urges to smoke) to a particular treatment at each of many time points.
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity
In this paper, we develop a Reinforcement Learning (RL) algorithm that continuously learns and improves the treatment policy embedded in the JITAI as the data is being collected from the user.
Personalizing Intervention Probabilities By Pooling
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.
