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Fairness-aware Regression Robust to Adversarial Attacks. (arXiv:2211.04449v1 [cs.CR])
Nov. 9, 2022, 2:20 a.m. | Yulu Jin, Lifeng Lai
cs.CR updates on arXiv.org arxiv.org
In this paper, we take a first step towards answering the question of how to
design fair machine learning algorithms that are robust to adversarial attacks.
Using a minimax framework, we aim to design an adversarially robust fair
regression model that achieves optimal performance in the presence of an
attacker who is able to add a carefully designed adversarial data point to the
dataset or perform a rank-one attack on the dataset. By solving the proposed
nonsmooth nonconvex-nonconcave minimax problem, …
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