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To be Robust and to be Fair: Aligning Fairness with Robustness

To be Robust and to be Fair: Aligning Fairness with Robustness

31 March 2023
Junyi Chai
Xiaoqian Wang
ArXivPDFHTML

Papers citing "To be Robust and to be Fair: Aligning Fairness with Robustness"

5 / 5 papers shown
Title
Multigroup Robustness
Multigroup Robustness
Lunjia Hu
Charlotte Peale
Judy Hanwen Shen
OOD
28
1
0
01 May 2024
Causality-Aided Trade-off Analysis for Machine Learning Fairness
Causality-Aided Trade-off Analysis for Machine Learning Fairness
Zhenlan Ji
Pingchuan Ma
Shuai Wang
Yanhui Li
FaML
26
7
0
22 May 2023
Group-Aware Threshold Adaptation for Fair Classification
Group-Aware Threshold Adaptation for Fair Classification
T. Jang
P. Shi
Xiaoqian Wang
FaML
78
36
0
08 Nov 2021
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
311
4,203
0
23 Aug 2019
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
233
673
0
17 Feb 2018
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