  Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing
the performance of a classifier for imbalanced data. AUC maximization refers to
a learning paradigm that learns a predictive model by directly maximizing its
AUC score. It has been studied for more than two decades dating back to late
90s and a huge amount of work has been devoted to AUC maximization since then.
Recently, stochastic AUC maximization for big data and deep AUC maximization
for deep learning have received increasing attention and yielded dramatic
impact for solving real-world problems. However, to the best our knowledge
there is no comprehensive survey of related works for AUC maximization. This
paper aims to address the gap by reviewing the literature in the past two
decades. We not only give a holistic view of the literature but also present
detailed explanations and comparisons of different papers from formulations to
algorithms and theoretical guarantees. We also identify and discuss remaining
and emerging issues for deep AUC maximization, and provide suggestions on
topics for future work.
