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Provably Fair Representations

Provably Fair Representations

12 October 2017
D. McNamara
Cheng Soon Ong
Robert C. Williamson
    FaML
ArXiv (abs)PDFHTML

Papers citing "Provably Fair Representations"

20 / 20 papers shown
Title
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort
Zhenpeng Chen
Jie M. Zhang
Mark Harman
Federica Sarro
FaMLAI4CE
109
177
0
14 Jul 2022
Individual Fairness Guarantees for Neural Networks
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
Fair Interpretable Learning via Correction Vectors
Mattia Cerrato
Marius Köppel
A. Segner
Stefan Kramer
FaMLCML
48
2
0
17 Jan 2022
Fair Representation: Guaranteeing Approximate Multiple Group Fairness
  for Unknown Tasks
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
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
Fair Representations by Compression
Xavier Gitiaux
Huzefa Rangwala
FaML
114
14
0
28 May 2021
Removing biased data to improve fairness and accuracy
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
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
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
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
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
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
A Distributed Fair Machine Learning Framework with Private Demographic Data Protection
Hui Hu
Yijun Liu
Zhen Wang
Chao Lan
FaMLFedML
86
26
0
17 Sep 2019
Fair Kernel Regression via Fair Feature Embedding in Kernel Space
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
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
On the Fairness of Disentangled Representations
Francesco Locatello
G. Abbati
Tom Rainforth
Stefan Bauer
Bernhard Schölkopf
Olivier Bachem
FaMLDRL
81
227
0
31 May 2019
The Frontiers of Fairness in Machine Learning
The Frontiers of Fairness in Machine Learning
Alexandra Chouldechova
Aaron Roth
FaML
205
416
0
20 Oct 2018
Hierarchical VampPrior Variational Fair Auto-Encoder
Hierarchical VampPrior Variational Fair Auto-Encoder
P. Botros
Jakub M. Tomczak
DRL
69
7
0
26 Jun 2018
POTs: Protective Optimization Technologies
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
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
388
685
0
17 Feb 2018
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