118 citations · 405 across the 29 of their papers we have counts for
6 papers · 1 filter
A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information
Cindy Trinh, Richard Combes
Motivated by applications in cognitive radio networks, we consider the decentralized multi-player multi-armed bandit problem, without collision nor sensing information. We propose…
An extension of McDiarmid's inequality
Richard Combes
We generalize McDiarmid's inequality for functions with bounded differences on a high probability set, using an extension argument. Those functions concentrate around their conditi…
Combinatorial Bandits Revisited
Richard Combes, M. Sadegh Talebi, Alexandre Proutiere +1
This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific reg…
Unimodal Bandits without Smoothness
Richard Combes, Alexandre Proutiere
We consider stochastic bandit problems with a continuous set of arms and where the expected reward is a continuous and unimodal function of the arm. No further assumption is made r…
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms
Richard Combes, Alexandre Proutiere
We consider stochastic multi-armed bandits where the expected reward is a unimodal function over partially ordered arms. This important class of problems has been recently investig…
Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms
Stefan Magureanu, Richard Combes, Alexandre Proutiere
We consider stochastic multi-armed bandit problems where the expected reward is a Lipschitz function of the arm, and where the set of arms is either discrete or continuous. For dis…