LIBAUC

LibAUC provides a unified framework to abstract the optimization of a family of risk functions called X-Risk, 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... We are a group of researchers who are fascinating about optimization algorithms for machine learning and making them practical for solving real-world problems! The people listed below are working or have been working in the lab of Optimization for Machine learning and AI (OptMAI Lab) at Texas A&M University directed by Professor Tianbao Yang. Our research centers around optimization, machine learning, big data, and artificial intelligence... Our Deep AUROC maximization method improves the baseline models by 4% for detecting Stroke on an internal data. Stroke is the 2nd leading cause for death globally, responsible for approximately 11% of total deaths. We..

Also known as: LibAUC.org