Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in
healthcare and the tech industry. They involve online learning algorithms that adaptively …

Multi-armed bandit has been well-known for its efficiency in online decision-making in terms
of minimizing the loss of the participants' welfare during experiments (ie, the regret). In …

Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-
commerce, healthcare, and policymaking because they can both improve outcomes for …

From clinical development of cancer therapies to investigations into partisan bias, adaptive
sequential designs have become increasingly popular method for causal inference, as they …

The past two decades have witnessed a surge of new research in the analysis of
randomized experiments. The emergence of this literature may seem surprising given the …

Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the
provision and type of support over time to an individual's changing status and contexts …

Sample-efficient offline reinforcement learning (RL) with linear function approximation has
been studied extensively recently. Much of the prior work has yielded instance-independent …

Indirect experiments provide a valuable framework for estimating treatment effects in
situations where conducting randomized control trials (RCTs) is impractical or unethical …

Principal component regression (PCR) is a popular technique for fixed-design error-in-
variables regression, a generalization of the linear regression setting in which the observed …

We prove that the continuous time version of the concentration bounds by Abbasi-Yadkori et
al.(2011) for adaptive linear regression cannot be improved in general, showing that there …