We propose EB-TC $\varepsilon $, a novel sampling rule for $\varepsilon $-best arm
identification in stochastic bandits. It is the first instance of Top Two algorithm analyzed for …

The problem of identifying the best arm among a collection of items having Gaussian
rewards distribution is well understood when the variances are known. Despite its practical …

In this paper we revisit the fixed-confidence identification of the Pareto optimal set in a multi-
objective multi-armed bandit model. As the sample complexity to identify the exact Pareto set …

Constructing nonasymptotic confidence intervals (CIs) for the mean of a univariate
distribution from independent and identically distributed (iid) observations is a fundamental …

We consider the best-k-arm identification problem for multi-armed bandits, where the
objective is to select the exact set of k arms with the highest mean rewards by sequentially …

A Top Two sampling rule for bandit identification is a method which selects the next arm to
sample from among two candidate arms, a leader and a challenger. Due to their simplicity …

We study the problem of best arm identification in linear bandits in the fixed-budget setting.
By leveraging properties of the G-optimal design and incorporating it into the arm allocation …

Abstract Best Arm Identification (BAI) problems are progressively used for data-sensitive
applications, such as designing adaptive clinical trials, tuning hyper-parameters, and …

Practitioners conducting adaptive experiments often encounter two competing priorities:
maximizing total welfare (orreward') through effective treatment assignment and swiftly …

Fixed-budget best-arm identification (BAI) is a bandit problem where the agent maximizes
the probability of identifying the optimal arm within a fixed budget of observations. In this …