no code implementations • 1 Apr 2024 • Xuran Li, Peng Wu, Yanting Chen, Xingjun Ma, Zhen Zhang, Kaixiang Dong
Deep neural networks (DNNs) are known to be sensitive to adversarial input perturbations, leading to a reduction in either prediction accuracy or individual fairness.
no code implementations • 1 Aug 2023 • Kaiqian Qu, Jia Ye, Xuran Li, Shuaishuai Guo
Integrated sensing and communication (ISAC) technology is one of the featuring technologies of the next-generation communication systems.
1 code implementation • 18 May 2023 • Xuran Li, Peng Wu, Kaixiang Dong, Zhen Zhang, Yanting Chen
This matrix categorizes predictions as true fair, true biased, false fair, and false biased, and the perturbations guided by it can produce a dual impact on instances and their similar counterparts to either undermine prediction accuracy (robustness) or cause biased predictions (individual fairness).
1 code implementation • 18 May 2022 • Xuran Li, Peng Wu, Jing Su
We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy.