2 papers
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…