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

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

This study investigates a local asymptotic minimax optimal strategy for fixed-budget best arm
identification (BAI). We propose the Adaptive Generalized Neyman Allocation (AGNA) …

We consider the fixed-confidence best arm identification (FC-BAI) problem in the Bayesian
Setting. This problem aims to find the arm of the largest mean with a fixed confidence level …

Recently multi-armed bandit problem arises in many real-life scenarios where arms must be
sampled in batches, due to limited time the agent can wait for the feedback. Such …

We study best-arm identification (BAI) in the fixed-budget setting. Adaptive allocations based
on upper confidence bounds (UCBs), such as UCBE, are known to work well in BAI …

This thesis contributes to the field of stochastic online learning problems, with a collection of
six papers each addressing unique aspects of online learning and inference problems …

This study investigates the contextual best arm identification (BAI) problem, aiming to design
an adaptive experiment to identify the best treatment arm conditioned on contextual …

In high-speed vehicular communications, eg, a high-speed train system, the beam between
the base station and user is frequently misaligned, resulting in a huge amount of associated …