Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity.

With the recent proliferation of mobile health technologies, health scientists are increasingly interested in developing just-in-time adaptive interventions (JITAIs), typically delivered via notifications on mobile devices and designed to help users prevent negative health outcomes and to promote the adoption and maintenance of healthy behaviors. A JITAI involves a sequence of decision rules (i.e.

Ensemble Estimation of Information Divergence .

Recent work has focused on the problem of nonparametric estimation of information divergence functionals between two continuous random variables. Many existing approaches require either restrictive assumptions about the density support set or difficult calculations at the support set boundary which must be known a priori. The mean squared error (MSE) convergence rate of a leave-one-out kernel density plug-in divergence functional estimator for general bounded density support sets is derived where knowledge of the support boundary, and therefore, the boundary correction is not required.

Action Centered Contextual Bandits.

Contextual bandits have become popular as they offer a middle ground between very simple approaches based on multi-armed bandits and very complex approaches using the full power of reinforcement learning. They have demonstrated success in web applications and have a rich body of associated theoretical guarantees. Linear models are well understood theoretically and preferred by practitioners because they are not only easily interpretable but also simple to implement and debug.