activity
20122022
most citedFair Regression with Wasserstein Barycenters

17 citations · 35 across the 9 of their papers we have counts for

collaborators

9 papers

math.ST2022

Prediction intervals with controlled length in the heteroscedastic Gaussian regression

Christophe Denis, Mohamed Hebiri, Ahmed Zaoui

We tackle the problem of building a prediction interval in heteroscedastic Gaussian regression. We focus on prediction intervals with constrained expected length in order to guaran…

stat.ML202114 cited

Set-valued classification -- overview via a unified framework

Evgenii Chzhen, Christophe Denis, Mohamed Hebiri +1

Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predi…

stat.ML202017 cited

Fair Regression with Wasserstein Barycenters

Evgenii Chzhen, Christophe Denis, Mohamed Hebiri +2

We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of t…

stat.ML2020

Regression with reject option and application to kNN

Christophe Denis, Mohamed Hebiri, Ahmed Zaoui

We investigate the problem of regression where one is allowed to abstain from predicting. We refer to this framework as regression with reject option as an extension of classificat…

stat.AP2019

A novel regularized approach for functional data clustering: An application to milking kinetics in dairy goats

C. Denis, E. Lebarbier, C. Lévy-Leduc +2

Motivated by an application to the clustering of milking kinetics of dairy goats, we propose in this paper a novel approach for functional data clustering. This issue is of growing…

math.ST2019

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

Evgenii Chzhen, Christophe Denis, Mohamed Hebiri +2

We study the problem of fair binary classification using the notion of Equal Opportunity. It requires the true positive rate to distribute equally across the sensitive groups. With…