25 citations · 90 across the 20 of their papers we have counts for
19 papers
Demographic Parity Tails for Regression
Naht Sinh Le, Christophe Denis, Mohamed Hebiri
Demographic parity (DP) is a widely studied fairness criterion in regression, enforcing independence between the predictions and sensitive attributes. However, constraining the ent…
Fair regression under localized demographic parity constraints
Arthur Charpentier, Christophe Denis, Romuald Elie +2
Demographic parity (DP) is a widely used group fairness criterion requiring predictive distributions to be invariant across sensitive groups. While natural in classification, full…
Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification
Lilian Say, Christophe Denis, Rafael Pinot
The increasing use of machine learning in sensitive applications demands algorithms that simultaneously preserve data privacy and ensure fairness across potentially sensitive sub-p…
Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Eyal Cohen, Christophe Denis, Mohamed Hebiri
Set-valued classification is used in multiclass settings where confusion between classes can occur and lead to misleading predictions. However, its application may amplify discrimi…
Empirical risk minimization algorithm for multiclass classification of S.D.E. paths
Christophe Denis, Eddy Ella Mintsa
We address the multiclass classification problem for stochastic diffusion paths, assuming that the classes are distinguished by their drift functions, while the diffusion coefficie…
Nonparametric plug-in classifier for multiclass classification of S.D.E. paths
Christophe Denis, Charlotte Dion-Blanc, Eddy Ella Mintsa +1
We study the multiclass classification problem where the features come from the mixture of time-homogeneous diffusions. Specifically, the classes are discriminated by their drift f…
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…
Active learning algorithm through the lens of rejection arguments
Christophe Denis, Mohamed Hebiri, Boris Ndjia Njike +1
Active learning is a paradigm of machine learning which aims at reducing the amount of labeled data needed to train a classifier. Its overall principle is to sequentially select th…
Fairness guarantee in multi-class classification
Christophe Denis, Romuald Elie, Mohamed Hebiri +1
Algorithmic Fairness is an established area of machine learning, willing to reduce the influence of hidden bias in the data. Yet, despite its wide range of applications, very few w…
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…
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…
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…
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…
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…
Minimax semi-supervised confidence sets for multi-class classification
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri
In this work we study the semi-supervised framework of confidence set classification with controlled expected size in minimax settings. We obtain semi-supervised minimax rates of c…
On the benefits of output sparsity for multi-label classification
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri +1
The multi-label classification framework, where each observation can be associated with a set of labels, has generated a tremendous amount of attention over recent years. The moder…
Confidence sets with expected sizes for Multiclass Classification
Christophe Denis, Mohamed Hebiri
Multiclass classification problems such as image annotation can involve a large number of classes. In this context, confusion between classes can occur, and single label classifica…
Consistency of plug-in confidence sets for classification in semi-supervised learning
Christophe Denis, Mohamed Hebiri
Confident prediction is highly relevant in machine learning; for example, in applications such as medical diagnoses, wrong prediction can be fatal. For classification, there alread…
Classification in postural style
Antoine Chambaz, Christophe Denis
This article contributes to the search for a notion of postural style, focusing on the issue of classifying subjects in terms of how they maintain posture. Longer term, the hope is…