Multi-armed bandits

Pareto Set Identification with Posterior Sampling
The problem of identifying the best answer among a collection of items having real-valued distribution is well-understood. Despite its …
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Best-Arm Identification in Unimodal Bandits
We study the fixed-confidence best-arm identification problem in unimodal bandits, in which the means of the arms increase with the …
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Solving Pure Exploration Problems with the Top Two Approach
In pure exploration problems for stochastic multi-armed bandits, the objective is to answer inquiries regarding a set of unknown …
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Differentially Private Best-Arm Identification
Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical …
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An Anytime Algorithm for Good Arm Identification
In good arm identification (GAI), the goal is to identify one arm whose average performance exceeds a given threshold, referred to as …
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An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond
We propose EB-TCε, a novel sampling rule for ε-best arm identification in stochastic bandits. It is the first instance of Top Two …
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On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence
Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical …
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Non-Asymptotic Analysis of a UCB-based Top Two Algorithm
A Top Two sampling rule for bandit identification is a method which selects the next arm to sample from among two candidate arms, a …
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Dealing with Unknown Variances in Best-Arm Identification
The problem of identifying the best arm among a collection of items having Gaussian rewards distribution is well understood when the …
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Top Two Algorithms Revisited
Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), …
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