Weakly Supervised AUC Optimization: A Unified Partial AUC Approach

arXiv:2305.14258v2 Announce Type: replace
Abstract: Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to as weak supervision. In this work, we present WSAUC, a unified framework for weakly supervised AUC optimization problems, which covers noisy label learning, positive-unlabeled learning, multi-instance learning, and semi-supervised learning scenarios. Within the WSAUC framework, we first frame the AUC optimization problems in various weakly supervised scenarios as a common formulation of minimizing the …

abstract arxiv auc cs.ai cs.lg framework machine machine learning optimization positive supervision tasks type work world