  The Area Under the ROC Curve (AUC) is a widely employed metric in long-tailed
classification scenarios. Nevertheless, most existing methods primarily assume
that training and testing examples are drawn i.i.d. from the same distribution,
which is often unachievable in practice. Distributionally Robust Optimization
(DRO) enhances model performance by optimizing it for the local worst-case
scenario, but directly integrating AUC optimization with DRO results in an
intractable optimization problem. To tackle this challenge, methodically we
propose an instance-wise surrogate loss of Distributionally Robust AUC (DRAUC)
and build our optimization framework on top of it. Moreover, we highlight that
conventional DRAUC may induce label bias, hence introducing distribution-aware
DRAUC as a more suitable metric for robust AUC learning. Theoretically, we
affirm that the generalization gap between the training loss and testing error
diminishes if the training set is sufficiently large. Empirically, experiments
on corrupted benchmark datasets demonstrate the effectiveness of our proposed
method. Code is available at: https://github.com/EldercatSAM/DRAUC.
