3 citations · 4 across the 5 of their papers we have counts for
5 papers
Layered State Discovery for Incremental Autonomous Exploration
Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric +1
We study the autonomous exploration (AX) problem proposed by Lim & Auer (2012). In this setting, the objective is to discover a set of -optimal policies reaching a set $\mathcal…
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…
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material
Matteo Pirotta, Simone Parisi, Marcello Restelli
This document contains supplementary material for the paper "Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation", published at the Twenty-Ninth AA…