Partial recovery bounds for clustering with the relaxed K-means. - Christophe Giraud 002; Nicolas Verzelen (CoRR, 2018)

Pair Matching: When bandits meet stochastic block model. - Christophe Giraud 002; Yann Issartel; Luc Lehéricy; Matthieu Lerasle (CoRR, 2019)

A Unified Approach to Fair Online Learning via Blackwell Approachability. - Evgenii Chzhen; Christophe Giraud 002; Gilles Stoltz (NeurIPS, 2021)

The price of unfairness in linear bandits with biased feedback. - Solenne Gaucher; Alexandra Carpentier; Christophe Giraud 002 (NeurIPS, 2022)

Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness. - Evgenii Chzhen; Christophe Giraud 002; Zhen Li; Gilles Stoltz (NeurIPS, 2023)

Computation-information gap in high-dimensional clustering. - Bertrand Even; Christophe Giraud 002; Nicolas Verzelen (COLT, 2024)

Estimating the history of a random recursive tree. - Simon Briend; Christophe Giraud 002; Gábor Lugosi; Déborah Sulem (CoRR, 2024)

Active clustering with bandit feedback. - Victor Thuot; Alexandra Carpentier; Christophe Giraud 002; Nicolas Verzelen (CoRR, 2024)

Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit. - Karl Hajjar; Lénaïc Chizat; Christophe Giraud 002 (J. Mach. Learn. Res., 2024)

Clustering with bandit feedback: breaking down the computation/information gap. - Victor Thuot; Alexandra Carpentier; Christophe Giraud 002; Nicolas Verzelen (ALT, 2025)