2 citations · 7 across the 12 of their papers we have counts for
12 papers
On Response-Adaptive Targeting Strategies for Multi-Treatment Experiments
Redouane Yagouti, Rémy Degenne, Emilie Kaufmann
Response-adaptive randomization (RAR) in clinical trials aims to improve ethical and statistical efficiency by dynamically allocating patients to treatments based on observed outco…
Best-Arm Identification in Unimodal Bandits
Riccardo Poiani, Marc Jourdan, Emilie Kaufmann +1
We study the fixed-confidence best-arm identification problem in unimodal bandits, in which the means of the arms increase with the index of the arm up to their maximum, then decre…
Optimal Multi-Fidelity Best-Arm Identification
Riccardo Poiani, Rémy Degenne, Emilie Kaufmann +2
In bandit best-arm identification, an algorithm is tasked with finding the arm with highest mean reward with a specified accuracy as fast as possible. We study multi-fidelity best-…
Finding good policies in average-reward Markov Decision Processes without prior knowledge
Adrienne Tuynman, Rémy Degenne, Emilie Kaufmann
We revisit the identification of an -optimal policy in average-reward Markov Decision Processes (MDP). In such MDPs, two measures of complexity have appeared in the li…
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…
On Elimination Strategies for Bandit Fixed-Confidence Identification
Andrea Tirinzoni, Rémy Degenne
Elimination algorithms for bandit identification, which prune the plausible correct answers sequentially until only one remains, are computationally convenient since they reduce th…
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification
Clémence Réda, Andrea Tirinzoni, Rémy Degenne
We study the problem of the identification of m arms with largest means under a fixed error rate (fixed-confidence Top-m identification), for misspecified linear bandit models.…