1

Best Arm Identification (BAI) algorithms are deployed in data-sensitive applications, such as adaptive clinical trials or user studies. …

Recent advances in language modeling have underscored the role of preference feedback in enhancing model performance. This paper …

The problem of identifying the best answer among a collection of items having real-valued distribution is well-understood. Despite its …

We study the fixed-confidence best-arm identification problem in unimodal bandits, in which the means of the arms increase with the …

We propose EB-TCε, a novel sampling rule for ε-best arm identification in stochastic bandits. It is the first instance of Top Two …

Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical …

A Top Two sampling rule for bandit identification is a method which selects the next arm to sample from among two candidate arms, a …

The problem of identifying the best arm among a collection of items having Gaussian rewards distribution is well understood when the …

Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), …

In pure-exploration problems, information is gathered sequentially to answer a question on the stochastic environment. While best-arm …