Near-Optimal Collaborative Learning in Bandits

Réda, Clémence; Vakili, Sattar; Kaufmann, Emilie

Document type :

Communication dans un congrès avec actes

Title :

Near-Optimal Collaborative Learning in Bandits

Author(s) :

Réda, Clémence [Auteur]
Scool [Scool]
Maladies neurodéveloppementales et neurovasculaires [NeuroDiderot (UMR_S_1141 / U1141)]
Vakili, Sattar [Auteur]
Kaufmann, Emilie [Auteur]

Scool [Scool]

Conference title :

NeurIPS 2022 - 36th Conference on Neural Information Processing System

City :

New Orleans

Country :

Etats-Unis d'Amérique

Start date of the conference :

2022-12

Journal title :

Advances in Neural Processing Systems

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

This paper introduces a general multi-agent bandit model in which each agent is facing a finite set of arms and may communicate with other agents through a central controller in order to identify-in pure exploration-or ...
Show more >This paper introduces a general multi-agent bandit model in which each agent is facing a finite set of arms and may communicate with other agents through a central controller in order to identify-in pure exploration-or play-in regret minimizationits optimal arm. The twist is that the optimal arm for each agent is the arm with largest expected mixed reward, where the mixed reward of an arm is a weighted sum of the rewards of this arm for all agents. This makes communication between agents often necessary. This general setting allows to recover and extend several recent models for collaborative bandit learning, including the recently proposed federated learning with personalization [30]. In this paper, we provide new lower bounds on the sample complexity of pure exploration and on the regret. We then propose a near-optimal algorithm for pure exploration. This algorithm is based on phased elimination with two novel ingredients: a data-dependent sampling scheme within each phase, aimed at matching a relaxation of the lower bound.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Au delà de l'apprentissage séquentiel pour de meilleures prises de décisions

Collections :