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 …

We study the problem of best-arm identification with fixed budget in stochastic two-arm
bandits with Bernoulli rewards. We prove that surprisingly, there is no algorithm that (i) …

We study a multi-objective pure exploration problem in a multi-armed bandit model. Each
arm is associated to an unknown multi-variate distribution and the goal is to identify the …

We study the batched best arm identification (BBAI) problem, where the learner's goal is to
identify the best arm while switching the policy as less as possible. In particular, we aim to …

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 …

We address the problem of best arm identification (BAI) with a fixed budget for two-armed
Gaussian bandits. In BAI, given multiple arms, we aim to find the best arm, an arm with the …

Practitioners conducting adaptive experiments often encounter two competing priorities:
reducing the cost of experimentation by effectively assigning treatments during the …

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

In good arm identification (GAI), the goal is to identify one arm whose average performance
exceeds a given threshold, referred to as good arm, if it exists. Few works have studied GAI …

We address the problem of lifelong fixed-budget best-arm identification (BAI), which arises in
realistic sequential A/B testing scenarios where the value of each arm is correlated across …