The topic of dynamic pricing and learning has received a considerable amount of attention
in recent years, from different scientific communities. We survey these literature streams: we …

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 …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Big data have enabled decision makers to tailor decisions at the individual level in a variety
of domains, such as personalized medicine and online advertising. Doing so involves …

A fundamental challenge in contextual bandits is to develop flexible, general-purpose
algorithms with computational requirements no worse than classical supervised learning …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

This work shows how to leverage causal inference to understand the behavior of complex
learning systems interacting with their environment and predict the consequences of …

In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play
one of K arms, each characterized by an unknown reward distribution. Reward realizations …

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as
well as the exploration method used, particularly in the presence of rich heterogeneity or …

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as
well as the exploration method used, particularly in the presence of rich heterogeneity or …