From 64dfc019516d3c7f842e85f7255a22c8347fa605 Mon Sep 17 00:00:00 2001 From: Zhuoning Yuan <38697082+yzhuoning@users.noreply.github.com> Date: Sun, 11 Jun 2023 15:50:31 -0700 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b129e29..32e7cc2 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ Why LibAUC? 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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