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Found 7 papers, 0 papers with code
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.
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.
Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health
For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users.
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.
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.
