arXiv:2510. 23469v2 Announce Type: replace Abstract: Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs).
By Yuhan Yang, Xingbo Fu, Jundong Li
arXiv:2404. 01356v3 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.
By Xuran Li, Hao Xue, Peng Wu, Xingjun Ma, Zhen Zhang, Huaming Chen, Flora D. Salim
arXiv:2606. 09881v1 Announce Type: new Abstract: Deepfake detectors show large performance gaps across demographic groups.
By Ryan Brown, Chris Russell
arXiv:2606. 31704v1 Announce Type: cross Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups.
By Maxime Moussi, Beno\^it Ronval, Siegfried Nijssen, F\'elicien Schiltz
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller
arXiv:2504. 21296v2 Announce Type: replace Abstract: Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques.
By Renqiang Luo, Huafei Huang, Ziqi Xu, Xikun Zhang, Enyan Dai, Bo Yang, Feng Xia