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cs.LG2026★ 1 cited
AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning
Pengxiang Wang, Hongbo Bo, Jun Hong +2
Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks. A number of…
cs.LG2025
Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning
Jingyu Hu, Hongbo Bo, Jun Hong +2
Graph Neural Networks (GNNs) often suffer from degree bias in node classification tasks, where prediction performance varies across nodes with different degrees. Several approaches…
cs.LG2024
ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups
Jingyu Hu, Jun Hong, Mengnan Du +1
Many bias mitigation methods have been developed for addressing fairness issues in machine learning. We found that using linear mixup alone, a data augmentation technique, for bias…