Balanced Self-Paced Learning for AUC Maximization. (arXiv:2207.03650v1 [cs.LG])

Learning to improve AUC performance is an important topic in machine
learning. However, AUC maximization algorithms may decrease generalization
performance due to the noisy data. Self-paced learning is an effective method
for handling noisy data. However, existing self-paced learning methods are
limited to pointwise learning, while AUC maximization is a pairwise learning
problem. To solve this challenging problem, we innovatively propose a balanced
self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide
a statistical objective for self-paced AUC. Based on …

arxiv auc learning lg