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This https://arxiv.org/abs/2504.11284 has been replaced.
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Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal Area Under the ROC Curve (AUC) against a single binary target label. However, one may often observe multiple binary target labels, e.g., from distinct human annotators. How can one synthesize such labels into a single coherent ranking? In this work, we formally analyze two approaches to this problem -- loss aggregation and label aggregation -- by characterizing their B…

ROC curves for LDA classifiers
Mateusz Krukowski
https://arxiv.org/abs/2507.18307 https://arxiv.org/pdf/2507.18307

ROC curves for LDA classifiers
In the paper, we derive an analytic formula for the ROC curves of the LDA classifiers. We establish elementary properties of these curves (monotonicity and concavity) and provide formulae for the area under curve (AUC) and the Youden J-index.