As bandit algorithms are increasingly utilized in scientific studies and industrial applications,
there is an associated increasing need for reliable inference methods based on the resulting …

It has become increasingly common for data to be collected adaptively, for example using
contextual bandits. Historical data of this type can be used to evaluate other treatment …

Abstract Response-Adaptive Randomization (RAR) is part of a wider class of data-
dependent sampling algorithms, for which clinical trials are typically used as a motivating …

In a wide variety of applications, including healthcare, bidding in first price auctions, digital
recommendations, and online education, it can be beneficial to learn a policy that assigns …

One of the key drivers of complexity in the classical (stochastic) multi-armed bandit (MAB)
problem is the difference between mean rewards in the top two arms, also known as the …

Fully Bayesian approaches to sequential decision-making assume that problem parameters
are generated from a known prior. In practice, such information is often lacking. This problem …

In bandit multiple hypothesis testing, each arm corresponds to a different null hypothesis that
we wish to test, and the goal is to design adaptive algorithms that correctly identify large set …

When data is collected in an adaptive manner, even simple methods like ordinary least
squares can exhibit non-normal asymptotic behavior. As an undesirable consequence …

Sequential data collection has emerged as a widely adopted technique for enhancing the
efficiency of data gathering processes. Despite its advantages, such data collection …

Motivated by practical needs in online experimentation and off-policy learning, we study the
problem of safe optimal design, where we develop a data logging policy that efficiently …