2 citations · 7 across the 12 of their papers we have counts for
6 papers · 1 filter
An -Best-Arm Identification Algorithm for Fixed-Confidence and Beyond
Marc Jourdan, Rémy Degenne, Emilie Kaufmann
We propose EB-TC, a novel sampling rule for -best arm identification in stochastic bandits. It is the first instance of Top Two algorithm analyzed for app…
On the Existence of a Complexity in Fixed Budget Bandit Identification
Rémy Degenne
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 dist…
Non-Asymptotic Analysis of a UCB-based Top Two Algorithm
Marc Jourdan, Rémy Degenne
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…
Dealing with Unknown Variances in Best-Arm Identification
Marc Jourdan, Rémy Degenne, Emilie Kaufmann
The problem of identifying the best arm among a collection of items having Gaussian rewards distribution is well understood when the variances are known. Despite its practical rele…
Choosing Answers in -Best-Answer Identification for Linear Bandits
Marc Jourdan, Rémy Degenne
In pure-exploration problems, information is gathered sequentially to answer a question on the stochastic environment. While best-arm identification for linear bandits has been ext…
Top Two Algorithms Revisited
Marc Jourdan, Rémy Degenne, Dorian Baudry +2
Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms. They select t…