Examples

Learning LibAUC: Advanced Examples

This section assumes that users have already completed the basic examples and have a solid understanding of the core functionality of LibAUC. In this section, it includes real-world dataset that demonstrate the versatility of LibAUC in solving complex problems.

• A Tutorial of Imbalanced Data Sampler (Updates Coming Soon)
• Constructing benchmark imbalanced datasets for CIFAR10, CIFAR100, CATvsDOG, STL10
• Using LibAUC with PyTorch learning rate scheduler
• Optimizing AUROC loss on Chest X-Ray dataset (CheXpert)
• Optimizing AUROC loss on Skin Cancer dataset (Melanoma)
• Optimizing AUROC loss on Molecular Graph dataset (OGB-Molhiv)
• Optimizing multi-label AUROC loss on Chest X-Ray dataset (CheXpert)
• Optimizing AUROC loss on Tabular dataset (Credit Fraud)
• Optimizing AUROC loss for Federated Learning