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 …

The remarkable instruction-following capability of large language models (LLMs) has
sparked a growing interest in automatically learning suitable prompts. However, while many …

Most linear experimental design problems assume homogeneous variance, while the
presence of heteroskedastic noise is present in many realistic settings. Let a learner have …

We study the real-valued combinatorial pure exploration of the multi-armed bandit in the
fixed-budget setting. We first introduce an algorithm named the Combinatorial Successive …

We investigate the fixed-budget best-arm identification (BAI) problem for linear bandits in a
potentially non-stationary environment. Given a finite arm set $\mathcal {X}\subset\mathbb …

This paper considers the problem of online clustering with bandit feedback. A set of arms (or
items) can be partitioned into various groups that are unknown. Within each group, the …

The remarkable instruction-following capability of large language models (LLMs) has
sparked a growing interest in automatically finding good prompts, ie, prompt optimization …

We study the problem of reliably identifying the best (lowest loss) arm in a stochastic multi-
armed bandit when the expected loss of each arm is monotone decreasing as a function of …

We study the problem of Bayesian fixed-budget best-arm identification (BAI) in structured
bandits. We propose an algorithm that uses fixed allocations based on the prior information …