Regression under demographic parity constraints via unlabeled post-processing - IRT SystemX Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2024

Regression under demographic parity constraints via unlabeled post-processing

Résumé

We address the problem of performing regression while ensuring demographic parity, even without access to sensitive attributes during inference. We present a general-purpose post-processing algorithm that, using accurate estimates of the regression function and a sensitive attribute predictor, generates predictions that meet the demographic parity constraint. Our method involves discretization and stochastic minimization of a smooth convex function. It is suitable for online post-processing and multi-class classification tasks only involving unlabeled data for the post-processing. Unlike prior methods, our approach is fully theory-driven. We require precise control over the gradient norm of the convex function, and thus, we rely on more advanced techniques than standard stochastic gradient descent. Our algorithm is backed by finite-sample analysis and post-processing bounds, with experimental results validating our theoretical findings.
Fichier principal
Vignette du fichier
neurips_2024.pdf (1.92 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04654182 , version 1 (19-07-2024)

Identifiants

  • HAL Id : hal-04654182 , version 1

Citer

Evgenii Chzhen, Mohamed Hebiri, Gayane Taturyan. Regression under demographic parity constraints via unlabeled post-processing. 2024. ⟨hal-04654182⟩
0 Consultations
0 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More