Evrard Garcelon | DeepAI

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∙ 03/07/2023

SALSA PICANTE: a machine learning attack on LWE with binary secrets

The Learning With Errors (LWE) problem is one of the major hard problems...

0 Cathy Li, et al. ∙

∙ 12/13/2021

Top K Ranking for Multi-Armed Bandit with Noisy Evaluations

We consider a multi-armed bandit setting where, at the beginning of each...

0 Evrard Garcelon, et al. ∙

∙ 12/11/2021

Privacy Amplification via Shuffling for Linear Contextual Bandits

Contextual bandit algorithms are widely used in domains where it is desi...

0 Evrard Garcelon, et al. ∙

∙ 12/02/2021

Differentially Private Exploration in Reinforcement Learning with Linear Representation

This paper studies privacy-preserving exploration in Markov Decision Pro...

0 Paul Luyo, et al. ∙

∙ 06/22/2021

A Unified Framework for Conservative Exploration

We study bandits and reinforcement learning (RL) subject to a conservati...

0 Yunchang Yang, et al. ∙

∙ 03/17/2021

Homomorphically Encrypted Linear Contextual Bandit

Contextual bandit is a general framework for online learning in sequenti...

0 Evrard Garcelon, et al. ∙

∙ 10/15/2020

Local Differentially Private Regret Minimization in Reinforcement Learning

Reinforcement learning algorithms are widely used in domains where it is...

0 Evrard Garcelon, et al. ∙

∙ 02/10/2020

Adversarial Attacks on Linear Contextual Bandits

Contextual bandit algorithms are applied in a wide range of domains, fro...

0 Evrard Garcelon, et al. ∙

∙ 02/08/2020

Improved Algorithms for Conservative Exploration in Bandits

In many fields such as digital marketing, healthcare, finance, and robot...

0 Evrard Garcelon, et al. ∙

∙ 02/08/2020

Conservative Exploration in Reinforcement Learning

While learning in an unknown Markov Decision Process (MDP), an agent sho...

0 Evrard Garcelon, et al. ∙

∙ 12/07/2019

No-Regret Exploration in Goal-Oriented Reinforcement Learning

Many popular reinforcement learning problems (e.g., navigation in a maze...

0 Jean Tarbouriech, et al. ∙

∙ 07/10/2018

Bandits with Side Observations: Bounded vs. Logarithmic Regret

We consider the classical stochastic multi-armed bandit but where, from ...

0 Rémy Degenne, et al. ∙

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