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…
Context Attentive Bandits: Contextual Bandit with Restricted Context
Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi +1
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accesse…
Reinforcement Learning Algorithm Selection
Romain Laroche, Raphael Feraud
This paper formalises the problem of online algorithm selection in the context of Reinforcement Learning. The setup is as follows: given an episodic task and a finite number of off…
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem
Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard
We consider a non-stationary formulation of the stochastic multi-armed bandit where the rewards are no longer assumed to be identically distributed. For the best-arm identification…
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…
A Neural Networks Committee for the Contextual Bandit Problem
Robin Allesiardo, Raphael Feraud, Djallel Bouneffouf
This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to…