Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization

15 August 2019

Papers citing "Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization"

9 / 9 papers shown

Title
Fair Text Classification via Transferable Representations Thibaud Leteno Michael Perrot Charlotte Laclau Antoine Gourru Christophe Gravier FaML 88 0 0 10 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 51 0 0 03 Feb 2025
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning Lucas de Lara Mathis Deronzier Alberto González Sanz Virgile Foy 22 0 0 12 Mar 2024
Weak Limits for Empirical Entropic Optimal Transport: Beyond Smooth Costs Alberto González Sanz Shayan Hundrieser OT 36 9 0 16 May 2023
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations? Fanny Jourdan Titon Tshiongo Kaninku Nicholas M. Asher Jean-Michel Loubes Laurent Risser FaML 28 4 0 27 Feb 2023
Linking convolutional kernel size to generalization bias in face analysis CNNs Hao Liang J. O. Caro Vikram Maheshri Ankit B. Patel Guha Balakrishnan CVBM CML 23 0 0 07 Feb 2023
An improved central limit theorem and fast convergence rates for entropic transportation costs E. del Barrio Alberto González Sanz Jean-Michel Loubes Jonathan Niles-Weed OT 36 32 0 19 Apr 2022
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 256 492 0 31 Dec 2020
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,092 0 24 Oct 2016