Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

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

Standard experimental designs are geared toward point estimation and hypothesis testing,
while bandit algorithms are geared toward in‐sample outcomes. Here, we instead consider …

Top two algorithms arose as an adaptation of Thompson sampling to best arm identification
in multi-armed bandit models for parametric families of arms. They select the next arm to …

Thompson sampling and other Bayesian sequential decision-making algorithms are among
the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The …

Digital twinning presents an exciting opportunity enabling real-time optimization of the
control and operations of cyber-physical systems (CPS) with data-driven simulations, while …

The expected improvement (EI) algorithm is a popular strategy for information collection in
optimization under uncertainty. The algorithm is widely known to be too greedy, but …

We introduce an adaptive targeted treatment assignment methodology for field experiments.
Our Tempered Thompson Algorithm balances the goals of maximizing the precision of …

We study the problem of active pure exploration with fixed confidence in generic stochastic
bandit environments. The goal of the learner is to answer a query about the environment …