Areas under the curves (AUC), including area under ROC curve (AUROC) and area under Precision-Recall curve (AUPRC) are widely used performance measures in many important computer vision and AI tasks and have extraordinarily varied applications in information retrieval, object detection, medical diagnosis, etc. AUROC is traditionally used as a measure for binary classification with imbalanced data. It has been used in many vision tasks, such as face verification, medical image classification. AUPRC or its variant called average precision (AP) is another popular measure for image/text retrieval, objective detection. Recently, there is a surge of research activities on developing efficient stochastic optimization algorithms and methods for deep AUC maximization, which directly minimize a surrogate loss of AUC for learning a deep neural network. The main objectives of this tutorial are three-fold:

• We will introduce state-of-the-art algorithms and techniques in the emerging area of optimizing the AUC scores with big data and for deep learning, and discuss new directions and opportunities for future work.
• We aim to provide a comprehensive algorithmic background that will allow AI/CV researchers and practitioners to use appropriate AUC maximization algorithms in their projects.
• The tutorial will introduce recent successes of deep AUC maximization in medical image classification (e.g., 1st Place at CheXpert Competition) by presenting the methods, algorithms, and library that play important roles in such successes.