Hinge Rank Loss and the Area Under the ROC Curve

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Gaussian Ranking by Matrix Factorization (2015, methodology): “This justiﬁes the hinge loss of the scores as to optimize AUC, as discussed in more detail in [16].”
Training and testing of recommender systems on data missing not at random (2010, methodology): “Given that ATOP ≈ AUC for N N (see above), we can motivate the likelihood of logistic regression as appropriate objective function for training: in [17], AUC was related to the so-called hinge loss, which is typically used for training support vector machines (SVM).”
Generalized Low Rank Models (2014, background): “terature that interpolate between the the two rst loss functions proposed above, or which prioritize correctly predicting the top ranked choices. These losses include the area under the 32 curve loss [Ste07], ordered weighted average of pairwise classication losses [UBG09], the weighted approximate-”
Bipartite Ranking through Minimization of Univariate Loss (2011, background): “Focusing on loss minimization in this paper, we shall mostly refer to a reciprocal measure, namely the rank loss defined as 1−AUC.”
AUC Maximization in the Era of Big Data and AI: A Survey (2022, unknown): “, supervised learning [81, 152], semi-supervised”