18 citations · 44 across the 13 of their papers we have counts for
13 papers
Conservative Exploration for Policy Optimization via Off-Policy Policy Evaluation
Paul Daoudi, Mathias Formoso, Othman Gaizi +2
A precondition for the deployment of a Reinforcement Learning agent to a real-world system is to provide guarantees on the learning process. While a learning algorithm will eventua…
SALSA PICANTE: a machine learning attack on LWE with binary secrets
Cathy Li, Jana Sotáková, Emily Wenger +4
Learning with Errors (LWE) is a hard math problem underpinning many proposed post-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism (KEM) standardized by NIS…
Privacy Amplification via Shuffling for Linear Contextual Bandits
Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet +1
Contextual bandit algorithms are widely used in domains where it is desirable to provide a personalized service by leveraging contextual information, that may contain sensitive inf…
Differentially Private Exploration in Reinforcement Learning with Linear Representation
Paul Luyo, Evrard Garcelon, Alessandro Lazaric +1
This paper studies privacy-preserving exploration in Markov Decision Processes (MDPs) with linear representation. We first consider the setting of linear-mixture MDPs (Ayoub et al.…
Top Ranking for Multi-Armed Bandit with Noisy Evaluations
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric +1
We consider a multi-armed bandit setting where, at the beginning of each round, the learner receives noisy independent, and possibly biased, \emph{evaluations} of the true reward o…
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
Yunchang Yang, Tianhao Wu, Han Zhong +5
In this paper, we present a reduction-based framework for conservative bandits and RL, in which our core technique is to calculate the necessary and sufficient budget obtained from…
Encrypted Linear Contextual Bandit
Evrard Garcelon, Vianney Perchet, Matteo Pirotta
Contextual bandit is a general framework for online learning in sequential decision-making problems that has found application in a wide range of domains, including recommendation…
Local Differential Privacy for Regret Minimization in Reinforcement Learning
Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke +1
Reinforcement learning algorithms are widely used in domains where it is desirable to provide a personalized service. In these domains it is common that user data contains sensitiv…
Improved Algorithms for Conservative Exploration in Bandits
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric +1
In many fields such as digital marketing, healthcare, finance, and robotics, it is common to have a well-tested and reliable baseline policy running in production (e.g., a recommen…
Adversarial Attacks on Linear Contextual Bandits
Evrard Garcelon, Baptiste Roziere, Laurent Meunier +4
Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education. In many of these domains, malicious…
Conservative Exploration in Reinforcement Learning
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric +1
While learning in an unknown Markov Decision Process (MDP), an agent should trade off exploration to discover new information about the MDP, and exploitation of the current knowled…
No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech, Evrard Garcelon, Michal Valko +2
Many popular reinforcement learning problems (e.g., navigation in a maze, some Atari games, mountain car) are instances of the episodic setting under its stochastic shortest path (…
Bandits with Side Observations: Bounded vs. Logarithmic Regret
Rémy Degenne, Evrard Garcelon, Vianney Perchet
We consider the classical stochastic multi-armed bandit but where, from time to time and roughly with frequency , an extra observation is gathered by the agent for free. We prov…