5 papers
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…
Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure
Ruiyi Fang, Shuo Wang, Ruizhi Pu +8
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods t…
Homophily Enhanced Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, Jingyu Zhao +5
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. In this paper, we highlight the…
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…
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
Ruizhi Pu, Gezheng Xu, Ruiyi Fang +3
Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning…