Dental disease continues to be one of the most prevalent chronic diseases in the United
States. Although oral self-care behaviors (OSCB), involving systematic twice-a-day tooth …

Mobile health aims to enhance health outcomes by delivering interventions to individuals as
they go about their daily life. The involvement of care partners and social support networks …

We consider the contextual bandit problem where at each time, the agent only has access to
a noisy version of the context and the error variance (or an estimator of this variance). This …

Online reinforcement learning and other adaptive sampling algorithms are increasingly used
in digital intervention experiments to optimize treatment delivery for users over time. In this …

We propose a novel nonparametric sequential test for composite hypotheses for means of
multiple data streams. Our proposed method,\emph {peeking with expectation-based …

Many standard estimators, when applied to adaptively collected data, fail to be
asymptotically normal, thereby complicating the construction of confidence intervals. We …

Online reinforcement learning (RL) algorithms offer great potential for personalizing
treatment for participants in clinical trials. However, deploying an online, autonomous …

Dental disease is still one of the most common chronic diseases in the United States. While
dental disease is preventable through healthy oral self-care behaviors (OSCB), this basic …

The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD),
poses a significant public health challenge globally. With a notably wide treatment gap …

Online Learning (OL) is a subfield of Machine Learning (ML) that is increasingly gaining
attention in academia and industry. A long-standing challenge in OL is the presence of …