118 citations · 405 across the 29 of their papers we have counts for
9 papers · 1 filter
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information
Wei Huang, Richard Combes, Cindy Trinh
We propose a novel algorithm for multi-player multi-armed bandits without collision sensing information. Our algorithm circumvents two problems shared by all state-of-the-art algor…
Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time
Thibaut Cuvelier, Richard Combes, Eric Gourdin
We consider combinatorial semi-bandits with uncorrelated Gaussian rewards. In this article, we propose the first method, to the best of our knowledge, that enables to compute the s…
On the Suboptimality of Thompson Sampling in High Dimensions
Raymond Zhang, Richard Combes
In this paper we consider Thompson Sampling (TS) for combinatorial semi-bandits. We demonstrate that, perhaps surprisingly, TS is sub-optimal for this problem in the sense that its…
Statistically Efficient, Polynomial Time Algorithms for Combinatorial Semi Bandits
Thibaut Cuvelier, Richard Combes, Eric Gourdin
We consider combinatorial semi-bandits over a set of arms where rewards are uncorrelated across items. For this problem, the algorithm ESCB yields the…
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Cindy Trinh, Emilie Kaufmann, Claire Vernade +1
Stochastic Rank-One Bandits (Katarya et al, (2017a,b)) are a simple framework for regret minimization problems over rank-one matrices of arms. The initially proposed algorithms are…
Computationally Efficient Estimation of the Spectral Gap of a Markov Chain
Richard Combes, Mikael Touati
We consider the problem of estimating from sample paths the absolute spectral gap of a reversible, irreducible and aperiodic Markov chain over a fi…
Minimal Exploration in Structured Stochastic Bandits
Richard Combes, Stefan Magureanu, Alexandre Proutiere
This paper introduces and addresses a wide class of stochastic bandit problems where the function mapping the arm to the corresponding reward exhibits some known structural propert…
A Minimax Optimal Algorithm for Crowdsourcing
Thomas Bonald, Richard Combes
We consider the problem of accurately estimating the reliability of workers based on noisy labels they provide, which is a fundamental question in crowdsourcing. We propose a novel…
A Streaming Algorithm for Crowdsourced Data Classification
Thomas Bonald, Richard Combes
We propose a streaming algorithm for the binary classification of data based on crowdsourcing. The algorithm learns the competence of each labeller by comparing her labels to those…