Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate

ICML 2018 Mingrui LiuXiaoxuan ZhangZaiyi ChenXiaoyu WangTianbao Yang

In this paper, we consider statistical learning with AUC (area under ROC curve) maximization in the classical stochastic setting where one random data drawn from an unknown distribution is revealed at each iteration for updating the model. Although consistent convex surrogate losses for AUC maximization have been proposed to make the problem tractable, it remains an challenging problem to design fast optimization algorithms in the classical stochastic setting due to that the convex surrogate loss depends on random pairs of examples from positive and negative classes... (read more)

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