Tianbao Yang

Associate Professor
Computer Science, University of Iowa
also affiliated with Applied Mathematical and Computational Sciences program
Email: [first-name]-[last-name] at uiowa.edu

I moved to Texas A&M University. Please check my new webpage.

LibAUC Library

The LibAUC Library is an open source library that we recently launched (April 2021). We have developed several practical and efficient algorithms for large-scale training for maximizing AUC score (including AUROC, AUPRC) for learning deep neural networks. Our DeepAUC method has achieved great success on various medical image datasets (e.g., 1st Place at Stanford CheXpert Competition) and biology datasets. 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 deepAUC methods on your data, we are glad to collaborate.

Birds: A Distributed Library for Classification and Regression

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:
T. Yang. Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent. NIPS, 2013.

The library can solve the following linear classification and regression problems:

  • SVM (hinge loss and squared hinge loss)
  • Logistic Regression
  • Least Square Regerssion
  • with L2 regularization, with/without L1 regularization
  • one-vs-all multi-class classification

Download Birds

The current version (version 3.0) can be obtained by downloading tgz file.