56 citations · 74 across the 7 of their papers we have counts for
7 papers
Efficient Algorithms for Extreme Bandits
Dorian Baudry, Yoan Russac, Emilie Kaufmann
In this paper, we contribute to the Extreme Bandit problem, a variant of Multi-Armed Bandits in which the learner seeks to collect the largest possible reward. We first study the c…
A/B/n Testing with Control in the Presence of Subpopulations
Yoan Russac, Christina Katsimerou, Dennis Bohle +3
Motivated by A/B/n testing applications, we consider a finite set of distributions (called \emph{arms}), one of which is treated as a \emph{control}. We assume that the population…
On Limited-Memory Subsampling Strategies for Bandits
Dorian Baudry, Yoan Russac, Olivier Cappé
There has been a recent surge of interest in nonparametric bandit algorithms based on subsampling. One drawback however of these approaches is the additional complexity required by…
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Louis Faury, Yoan Russac, Marc Abeille +1
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity. In this paper we st…
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Yoan Russac, Louis Faury, Olivier Cappé +1
Contextual sequential decision problems with categorical or numerical observations are ubiquitous and Generalized Linear Bandits (GLB) offer a solid theoretical framework to addres…
Algorithms for Non-Stationary Generalized Linear Bandits
Yoan Russac, Olivier Cappé, Aurélien Garivier
The statistical framework of Generalized Linear Models (GLM) can be applied to sequential problems involving categorical or ordinal rewards associated, for instance, with clicks, l…
Weighted Linear Bandits for Non-Stationary Environments
Yoan Russac, Claire Vernade, Olivier Cappé
We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regressi…