  Stochastic AUC maximization has garnered an increasing interest due to better
fit to imbalanced data classification. However, existing works are limited to
stochastic AUC maximization with a linear predictive model, which restricts its
predictive power when dealing with extremely complex data. In this paper, we
consider stochastic AUC maximization problem with a deep neural network as the
predictive model. Building on the saddle point reformulation of a surrogated
loss of AUC, the problem can be cast into a {\it non-convex concave} min-max
problem. The main contribution made in this paper is to make stochastic AUC
maximization more practical for deep neural networks and big data with
theoretical insights as well. In particular, we propose to explore
Polyak-\L{}ojasiewicz (PL) condition that has been proved and observed in deep
learning, which enables us to develop new stochastic algorithms with even
faster convergence rate and more practical step size scheme. An AdaGrad-style
algorithm is also analyzed under the PL condition with adaptive convergence
rate. Our experimental results demonstrate the effectiveness of the proposed
algorithms.
