6 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…
Randomized multi-class classification under system constraints: a unified approach via post-processing
Evgenii Chzhen, Mohamed Hebiri, Gayane Taturyan
We study the problem of multi-class classification under system-level constraints expressible as linear functionals over randomized classifiers. We propose a post-processing approa…
Class conditional conformal prediction for multiple inputs by p-value aggregation
Jean-Baptiste Fermanian, Mohamed Hebiri, Joseph Salmon
Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. This work introduces an inn…
EERO: Early Exit with Reject Option for Efficient Classification with limited budget
Florian Valade, Mohamed Hebiri, Paul Gay
The increasing complexity of advanced machine learning models requires innovative approaches to manage computational resources effectively. One such method is the Early Exit strate…
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…