3 papers
cs.CV2026
Full spectrum Unlearnable Examples via Spectral Equalization
Jiale Cai, Gezheng Xu, Zhihao Li +6
Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that…
cs.LG2026
Fair Dataset Distillation via Cross-Group Barycenter Alignment
Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei +2
Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit dist…
cs.LG2025
On the Benefits of Attribute-Driven Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, Zhao Kang +5
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Rece…