Fair Data Representation for Machine Learning at the Pareto Frontier

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Fair Data Representation for Machine Learning at the Pareto Frontier

2 January 2022

Thomas Strohmer

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Papers citing "Fair Data Representation for Machine Learning at the Pareto Frontier"

8 / 8 papers shown

Title
Fair Regression with Wasserstein Barycenters Evgenii Chzhen Christophe Denis Mohamed Hebiri L. Oneto Massimiliano Pontil 80 108 0 12 Jun 2020
Wasserstein Fair Classification Ray Jiang Aldo Pacchiano T. Stepleton Heinrich Jiang Silvia Chiappa 69 181 0 28 Jul 2019
The Frontiers of Fairness in Machine Learning Alexandra Chouldechova Aaron Roth FaML 194 416 0 20 Oct 2018
A Convex Framework for Fair Regression R. Berk Hoda Heidari S. Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel Aaron Roth FaML 117 342 0 07 Jun 2017
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi FaML 205 1,213 0 26 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 233 4,330 0 07 Oct 2016
A fixed-point approach to barycenters in Wasserstein space P. C. Álvarez-Esteban E. del Barrio J. A. Cuesta-Albertos Carlos Matrán 71 193 0 17 Nov 2015
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 206 1,993 0 11 Dec 2014

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