AI RESEARCH
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
arXiv CS.LG
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ArXi:2605.01874v1 Announce Type: new The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that, in noisy settings, there may exist a subset of the