no code implementations • 16 Mar 2023 • Rémy Degenne
We show that there is no such complexity for several fixed budget identification tasks including Bernoulli best arm identification with two arms: there is no single algorithm that attains everywhere the best possible rate.
no code implementations • 3 Oct 2022 • 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.
no code implementations • 13 Jun 2022 • Marc Jourdan, Rémy Degenne, Dorian Baudry, Rianne de Heide, Emilie Kaufmann
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.
no code implementations • 9 Jun 2022 • Marc Jourdan, Rémy Degenne
In pure-exploration problems, information is gathered sequentially to answer a question on the stochastic environment.
1 code implementation • 22 May 2022 • 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 the problem size over time.
1 code implementation • NeurIPS 2021 • Clémence Réda, Andrea Tirinzoni, Rémy Degenne
In this work, we first derive a tractable lower bound on the sample complexity of any $\delta$-correct algorithm for the general Top-m identification problem.
no code implementations • NeurIPS 2021 • Reda Ouhamma, Rémy Degenne, Pierre Gaillard, Vianney Perchet
In the fixed budget thresholding bandit problem, an algorithm sequentially allocates a budgeted number of samples to different distributions.
no code implementations • ICML 2020 • Rémy Degenne, Han Shao, Wouter M. Koolen
We study reward maximisation in a wide class of structured stochastic multi-armed bandit problems, where the mean rewards of arms satisfy some given structural constraints, e. g. linear, unimodal, sparse, etc.
no code implementations • ICML 2020 • Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
We investigate an active pure-exploration setting, that includes best-arm identification, in the context of linear stochastic bandits.
no code implementations • NeurIPS 2019 • Rémy Degenne, Wouter M. Koolen, Pierre Ménard
Pure exploration (aka active testing) is the fundamental task of sequentially gathering information to answer a query about a stochastic environment.
no code implementations • NeurIPS 2019 • Rémy Degenne, Wouter M. Koolen
We present a new algorithm which extends Track-and-Stop to the multiple-answer case and has asymptotic sample complexity matching the lower bound.
no code implementations • 9 Oct 2018 • Rémy Degenne, Thomas Nedelec, Clément Calauzènes, Vianney Perchet
State of the art online learning procedures focus either on selecting the best alternative ("best arm identification") or on minimizing the cost (the "regret").
no code implementations • 10 Jul 2018 • Rémy Degenne, Evrard Garcelon, Vianney Perchet
We consider the classical stochastic multi-armed bandit but where, from time to time and roughly with frequency $\epsilon$, an extra observation is gathered by the agent for free.
no code implementations • NeurIPS 2016 • Rémy Degenne, Vianney Perchet
We introduce a way to quantify the dependency structure of the problem and design an algorithm that adapts to it.