This paper is concerned with identifying the arm with the highest mean in a multi-armed
bandit problem using as few independent samples from the arms as possible. While the so …

Motivated by the task of hyperparameter optimization, we introduce the\em non-stochastic
best-arm identification problem. We identify an attractive algorithm for this setting that makes …

We give a complete characterization of the complexity of best-arm identification in one-
parameter bandit problems. We prove a new, tight lower bound on the sample complexity …

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in
many different contexts in statistics and machine learning. Whereas the achievable limit in …

We study the problem of exploration in stochastic Multi-Armed Bandits. Even in the simplest
setting of identifying the best arm, there remains a logarithmic multiplicative gap between the …

The paper proposes a novel upper confidence bound (UCB) procedure for identifying the
arm with the largest mean in a multi-armed bandit game in the fixed confidence setting using …

We study the problem of identifying the best arm (s) in the stochastic multi-armed bandit
setting. This problem has been studied in the literature from two different perspectives: fixed …

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 study the {\em combinatorial pure exploration (CPE)} problem in the stochastic multi-
armed bandit setting, where a learner explores a set of arms with the objective of identifying …

We study a specific combinatorial pure exploration stochastic bandit problem where the
learner aims at finding the set of arms whose means are above a given threshold, up to a …