10 citations · 14 across the 10 of their papers we have counts for
6 papers · 1 filter
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits
Sharyal Zafar, Raphaël Feraud, Anne Blavette +2
The drastic growth of electric vehicles and photovoltaics can introduce new challenges, such as electrical current congestion and voltage limit violations due to peak load demands.…
Batched Bandits with Crowd Externalities
Romain Laroche, Othmane Safsafi, Raphael Feraud +1
In Batched Multi-Armed Bandits (BMAB), the policy is not allowed to be updated at each time step. Usually, the setting asserts a maximum number of allowed policy updates and the al…
Double-Linear Thompson Sampling for Context-Attentive Bandits
Djallel Bouneffouf, Raphaël Féraud, Sohini Upadhyay +2
In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog s…
Decentralized Exploration in Multi-Armed Bandits -- Extended version
Raphaël Féraud, Réda Alami, Romain Laroche
We consider the decentralized exploration problem: a set of players collaborate to identify the best arm by asynchronously interacting with the same stochastic environment. The obj…
Multi-armed Bandit Problem with Known Trend
Djallel Bouneffouf, Raphaël Feraud
We consider a variant of the multi-armed bandit model, which we call multi-armed bandit problem with known trend, where the gambler knows the shape of the reward function of each a…
Random Forest for the Contextual Bandit Problem - extended version
Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy +1
To address the contextual bandit problem, we propose an online random forest algorithm. The analysis of the proposed algorithm is based on the sample complexity needed to find the…