Provably Fair Representations

12 October 2017

Papers citing "Provably Fair Representations"

20 / 20 papers shown

Title
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey Max Hort Zhenpeng Chen Jie M. Zhang Mark Harman Federica Sarro FaML AI4CE 107 177 0 14 Jul 2022
Individual Fairness Guarantees for Neural Networks Elias Benussi A. Patané Matthew Wicker Luca Laurenti Marta Kwiatkowska University of Oxford 68 22 0 11 May 2022
Fair Interpretable Learning via Correction Vectors Mattia Cerrato Marius Köppel A. Segner Stefan Kramer FaML CML 48 2 0 17 Jan 2022
Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks Xudong Shen Yongkang Wong Mohan S. Kankanhalli FaML 95 20 0 01 Sep 2021
A Clarification of the Nuances in the Fairness Metrics Landscape Alessandro Castelnovo Riccardo Crupi Greta Greco D. Regoli Ilaria Giuseppina Penco A. Cosentini FaML 59 192 0 01 Jun 2021
Fair Representations by Compression Xavier Gitiaux Huzefa Rangwala FaML 114 14 0 28 May 2021
Removing biased data to improve fairness and accuracy Sahil Verma Michael Ernst René Just FaML 123 25 0 05 Feb 2021
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation Umang Gupta Aaron Ferber B. Dilkina Greg Ver Steeg 113 58 0 11 Jan 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 324 500 0 31 Dec 2020
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately Fereshte Khani Percy Liang FaML 61 66 0 07 Dec 2020
Learning Certified Individually Fair Representations Anian Ruoss Mislav Balunović Marc Fischer Martin Vechev FaML 67 96 0 24 Feb 2020
Think Locally, Act Globally: Federated Learning with Local and Global Representations Paul Pu Liang Terrance Liu Liu Ziyin Nicholas B. Allen Randy P. Auerbach David Brent Ruslan Salakhutdinov Louis-Philippe Morency FedML 122 569 0 06 Jan 2020
A Distributed Fair Machine Learning Framework with Private Demographic Data Protection Hui Hu Yijun Liu Zhen Wang Chao Lan FaML FedML 86 26 0 17 Sep 2019
Fair Kernel Regression via Fair Feature Embedding in Kernel Space Austin Okray Hui Hu Chao Lan FaML 80 4 0 04 Jul 2019
Learning Fair and Transferable Representations L. Oneto Michele Donini Andreas Maurer Massimiliano Pontil FaML 88 19 0 25 Jun 2019
On the Fairness of Disentangled Representations Francesco Locatello G. Abbati Tom Rainforth Stefan Bauer Bernhard Schölkopf Olivier Bachem FaML DRL 81 227 0 31 May 2019
The Frontiers of Fairness in Machine Learning Alexandra Chouldechova Aaron Roth FaML 205 416 0 20 Oct 2018
Hierarchical VampPrior Variational Fair Auto-Encoder P. Botros Jakub M. Tomczak DRL 69 7 0 26 Jun 2018
POTs: Protective Optimization Technologies B. Kulynych R. Overdorf Carmela Troncoso Seda F. Gürses 115 97 0 07 Jun 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 388 685 0 17 Feb 2018