10 citations · 14 across the 5 of their papers we have counts for
6 papers
On the Impact of Output Perturbation on Fairness in Binary Linear Classification
Vitalii Emelianov, Michaël Perrot
We theoretically study how differential privacy interacts with both individual and group fairness in binary linear classification. More precisely, we focus on the output perturbati…
A Revenue Function for Comparison-Based Hierarchical Clustering
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{Objec…
FairGrad: Fairness Aware Gradient Descent
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. Most exist…
Near-Optimal Comparison Based Clustering
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. How…
Foundations of Comparison-Based Hierarchical Clustering
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. In…
Boosting for Comparison-Based Learning
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 is closer to o…