1 code implementation • ICML 2020 • Michael Lohaus, Michaël Perrot, Ulrike Von Luxburg
We address the problem of classification under fairness constraints.
no code implementations • 5 Feb 2024 • Vitalii Emelianov, Michaël Perrot
We theoretically study how differential privacy interacts with both individual and group fairness in binary linear classification.
1 code implementation • 29 Nov 2022 • Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar
Comparison-based learning addresses the problem of learning when, instead of explicit features or pairwise similarities, one only has access to comparisons of the form: \emph{Object $A$ is more similar to $B$ than to $C$.}
1 code implementation • 28 Oct 2022 • Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi
We theoretically study the impact of differential privacy on fairness in classification.
no code implementations • 22 Jun 2022 • Gaurav Maheshwari, Michaël Perrot
We address the problem of group fairness in classification, where the objective is to learn models that do not unjustly discriminate against subgroups of the population.
no code implementations • NeurIPS 2020 • Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
The goal of clustering is to group similar objects into meaningful partitions.
1 code implementation • NeurIPS 2019 • Debarghya Ghoshdastidar, Michaël Perrot, Ulrike Von Luxburg
We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities.
no code implementations • 31 Oct 2018 • Michaël Perrot, Ulrike Von Luxburg
We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object $x_i$ is closer to object $x_j$ than to object $x_k$."
no code implementations • NeurIPS 2016 • Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard
Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling $\mgamma$ but do not address the problem of learning the underlying transport map $\funcT$ linked to the original Monge problem.
no code implementations • NeurIPS 2015 • Michaël Perrot, Amaury Habrard
In this paper, instead of bringing closer examples of the same class and pushing far away examples of different classes we propose to move the examples with respect to virtual points.