Tianbao Yang
Associate Professor and Herbert H. Richardson Faculty FellowComputer Science and Engineering, Texas A&M University
Email: [first-name]-[last-name] at tamu.edu
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Tianbao YangAssociate Professor and Herbert H. Richardson Faculty FellowComputer Science and Engineering, Texas A&M University Email: [first-name]-[last-name] at tamu.edu |
The LibAUC Library is an open source library that we launched in April 2021. We have developed several practical and efficient algorithms for large-scale training of deep neural networks for optimizing a family of X-risks (e.g., AUROC, partial AUC, AUPRC, AP, NDCG, Contrastive Loss) for learning deep neural networks. Our LibAUC library has achieved great success on various problems (e.g., 1st Place at Stanford CheXpert Competition, 1st Place at MIT AICures Challenge, beating Tensorflow Ranking Library). We will continuously develop the library to benefit the community. For more detials, please check our LibAUC library website. If you are interested in the research of LibAUC on your data, we are glad to collaborate.
The birds library is a distributed library aiming to solve big data classification and regression problems in a distributed enviorment (a cluster of machines) or parallel fashion (a multi-core machine). It implements a practical distributed stochastic dual coordinate ascent proposed in the following paper: Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent published at NIPS, 2013. The two ideas proposed in this work namely local updates and model averaging have become the norm of Federated Learning.
The library can solve the following linear classification and regression problems: