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 …

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 …

We investigate an active\emph {pure-exploration} setting, that includes\emph {best-arm
identification}, in the context of\emph {linear stochastic bandits}. While asymptotically optimal …

This paper presents new deviation inequalities that are valid uniformly in time under
adaptive sampling in a multi-armed bandit model. The deviations are measured using the …

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 …

The theory of reinforcement learning has focused on two fundamental problems: achieving
low regret, and identifying $\epsilon $-optimal policies. While a simple reduction allows one …

Pure exploration (aka active testing) is the fundamental task of sequentially gathering
information to answer a query about a stochastic environment. Good algorithms make few …

We consider the problem of identifying the best arm in stochastic Multi-Armed Bandits
(MABs) using a fixed sampling budget. Characterizing the minimal instance-specific error …

In fixed budget bandit identification, an algorithm sequentially observes samples from
several distributions up to a given final time. It then answers a query about the set of …

We consider the development of adaptive, instance-dependent algorithms for interactive
decision making (bandits, reinforcement learning, and beyond) that, rather than only …