We study bandit model selection in stochastic environments. Our approach relies on a
master algorithm that selects between candidate base algorithms. We develop a master …

Individual human decision-makers may benefit from different forms of support to improve
decision outcomes, but when each form of support will yield better outcomes? In this work …

We study nonparametric contextual bandits where Lipschitz mean reward functions may
change over time. We first establish the minimax dynamic regret rate in this less understood …

In the evolving landscape of digital commerce, adaptive dynamic pricing strategies are
essential for gaining a competitive edge. This paper introduces novel {\em doubly …

A fundamental question for companies with large amount of logged data is: How to use such
logged data together with incoming streaming data to make good decisions? Many …

We study the best-arm identification problem with fixed confidence when contextual
(covariate) information is available in stochastic bandits. Although we can use contextual …

We study the fundamental limits of learning in contextual bandits, where a learner's rewards
depend on their actions and a known context, which extends the canonical multi-armed …

This article studies an important sequential decision making problem known as the multi-
armed stochastic bandit problem with covariates. Under a linear bandit framework with high …

Contextual bandits constitute a classical framework for decision-making under uncertainty.
In this setting, the goal is to learn the arms of highest reward subject to contextual …

Bandits with covariates, aka\emph {contextual bandits}, address situations where optimal
actions (or arms) at a given time $ t $, depend on a\emph {context} $ x_t $, eg, a new …