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cs.LG2025
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Ruofan Hu, Dongyu Zhang, Huayi Zhang +1
Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labele…
cs.LG2024
Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics
Oluseun Olulana, Kathleen Cachel, Fabricio Murai +1
As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (L…
cs.LG2024
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
Dongyu Zhang, Ruofan Hu, Elke Rundensteiner
Deep neural networks (DNNs) have advanced many machine learning tasks, but their performance is often harmed by noisy labels in real-world data. Addressing this, we introduce CoLaf…