Stochastic Proximal Algorithms for AUC Maximization
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:37103719, 2018.
Abstract
Stochastic optimization algorithms such as SGDs update the model sequentially with cheap periteration costs, making them amenable for largescale data analysis. However, most of the existing studies focus on the classification accuracy which can not be directly applied to the important problems of maximizing the Area under the ROC curve (AUC) in imbalanced classification and bipartite ranking. In this paper, we develop a novel stochastic proximal algorithm for AUC maximization which is referred to as SPAM. Compared with the previous literature, our algorithm SPAM applies to a nonsmooth penalty function, and achieves a convergence rate of O(log t/t) for strongly convex functions while both space and periteration costs are of one datum.
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