10 citations · 14 across the 10 of their papers we have counts for
12 papers
Reinforcement Learning based DBMS Buffer Pool Auto-Tuning for Optimal Memory Utilization
Yifan Wang, Patrick Royer, Raphaël Féraud +1
Administering Database Management Systems (DBMS) instances requires Database Administrators (DBA) to balance performance in terms of Service Level Agreement (SLA) against resource…
Survey: Multi-Armed Bandits Meet Large Language Models
Djallel Bouneffouf, Raphael Feraud
Bandit algorithms and Large Language Models (LLMs) have emerged as powerful tools in artificial intelligence, each addressing distinct yet complementary challenges in decision-maki…
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…