This article introduces the WSAUC framework for weakly supervised AUC optimization, covering various scenarios like noisy label learning, positive-unlabeled learning, multi-instance learning, and semi-supervised learning. The authors propose a reversed partial AUC (rpAUC) as a robust training objective for AUC maximization in the presence of contaminated labels. Theoretical and experimental results support the effectiveness of WSAUC in weakly supervised AUC optimization tasks. The content is structured into sections discussing the introduction, related work, unified formulation, theoretical analysis, and practical applications.
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by Zheng Xie,Yu... klokken arxiv.org 03-28-2024
https://arxiv.org/pdf/2305.14258.pdfDypere Spørsmål