LibAUC offers an easier way to directly optimize commonly-used performance measures and losses with user-friendly API. LibAUC has broad applications in AI for tackling many challenges, such as **Classification of Imbalanced Data (CID)**, **Learning to Rank (LTR)**, and **Contrastive Learning of Representation (CLR)**. LibAUC provides a unified framework to abstract the optimization of many compositional loss functions, including surrogate losses for AUROC, AUPRC/AP, and partial AUROC that are suitable for CID, surrogate losses for NDCG, top-K NDCG, and listwise losses that are used in LTR, and global contrastive losses for CLR. Here’s an overview:
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