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 …

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 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 consider selecting the top-m alternatives from a finite number of alternatives via Monte
Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a …

Given a fixed simulation budget, the problem of selecting the best and top-m alternatives
among a finite set of alternatives have been studied separately in simulation optimization …

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

Top-$2 $ methods have become popular in solving the best arm identification (BAI) problem.
The best arm, or the arm with the largest mean amongst finitely many, is identified through …

We aim to efficiently allocate a fixed simulation budget to identify the top-mc designs for
each context among a finite number of contexts. The performance of each design under a …

We consider the experimental design with the objective of identifying the potentially
heterogeneous optimal treatment for each subgroup, utilizing minimal experimental units …

This thesis contributes to the field of stochastic bandits by exploring the fundamental limits
(information-theoretic lower bounds) of three prevalent objects in various reinforcement …