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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…
cs.LG2024
Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning
Jingyu Hu, Weiru Liu, Mengnan Du
Recent studies highlight the effectiveness of using in-context learning (ICL) to steer large language models (LLMs) in processing tabular data, a challenging task given the structu…