Adaptive experimental designs can dramatically improve efficiency in randomized trials. But
with adaptively collected data, common estimators based on sample means and inverse …

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

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 …

During online decision making in Multi-Armed Bandits (MAB), one needs to conduct
inference on the true mean reward of each arm based on data collected so far at each step …

It is well known that in stochastic multi-armed bandits (MAB), the sample mean of an arm is
typically not an unbiased estimator of its true mean. In this paper, we decouple three …

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 …

Adaptive collection of data is commonplace in applications throughout science and
engineering. From the point of view of statistical inference, however, adaptive data collection …

The study of experimental design offers tremendous benefits for answering causal questions
across a wide range of applications, including agricultural experiments, clinical trials …

Adaptive experimental design methods are increasingly being used in industry as a tool to
boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing …