AUC Optimization from Multiple Unlabeled Datasets - Xie, Zheng; Liu, Yu; Li, Ming (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
Weakly supervised learning aims to make machine learning more powerful when the perfect supervision is unavailable, and has attracted much attention from researchers. Among the various scenarios of weak supervision, one of the most challenging cases is learning from multiple unlabeled (U) datasets with only a little knowledge of the class priors, or U^m learning for short. In this paper, we study the problem of building an AUC (area under ROC curve) optimal model from multiple unlabeled datasets, which maximizes the pairwise ranking ability of the classifier. We propose U^m-AUC, an AUC optimization approach that converts the U^m data into a multi-label AUC optimization problem, and can be trained efficiently. We show that the proposed U^m-AUC is effective theoretically and empirically.