Machine learning has emerged to be an indispensable tool for addressing many decision-making problems, e.g., autonomous driving. As applications of machine learning algorithms for decision-making broaden and diversify, the requirements on security, fairness, interpretability and generalization have been pushed to higher standards. These emerging issues have brought great challenges to the design of machine learning algorithms in the presence of big and complex data. Traditional machine learning methods by minimizing an unconstrained or simply constrained convex objective have become increasingly unsatisfactory. This project seeks to advance learning with complex objectives and constraints by designing and analyzing efficient and effective optimization algorithms for addressing computational challenges in new machine learning paradigms. The project will enhance the ability to solve large-scale, real-world problems from more diverse and broad applications. Furthermore, the project will strive to communicate the significance of machine learning and optimization and provide excellent research experience to students at different levels.

 

Students

1. Mingrui Liu (joined the project since 2019 August)
2. Zhuoning Yuan (joined the project since 2019 September)
3. Zhishuai Guo (joined the project since 2019 August)

Publications

1. Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks.
   Zhishuai Guo*, Mingrui Liu*, Zhuoning Yuan*, Li Shen, Wei Liu, Tianbao Yang
   To Appear in ICML 2020
   
   
2. Proximally Constrained Methods for Weakly Convex Optimization with Weakly Convex Constraints.
   Runchao Ma, Qihang Lin, Tianbao Yang
   To Appear in ICML 2020
   
   
3. Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets.
   Mingrui Liu*, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
   In ICLR 2020
   
   
4. Stochastic AUC Maximization with Deep Neural Networks.
   Mingrui Liu*, Zhuoning Yuan*, Yiming Ying, Tianbao Yang
   In ICLR 2020
   
   
5. A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.
   Qihang Lin, Selvaprabu Nadarajah, Negar Soheili, Tianbao Yang
   To Appear in JMLR
   
   

Talks/Presentations

1. (ICLR 2020) Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets.
   Mingrui Liu*, Youssef Mroueh, Jerret Ross, Wei Zhang, Xiaodong Cui, Payel Das, Tianbao Yang
   
   
2. (ICLR 2020) Stochastic AUC Maximization with Deep Neural Networks.
   Mingrui Liu*, Zhuoning Yuan*, Yiming Ying, Tianbao Yang
   
   

Software

Deep AUC Maximization: This implemented a stochastic min-max optimization algorithm for learning a deep neural network by maximizing AUC. [Code] [Paper]

Distributed Deep AUC Maximization: This implemented a communication efficient distributed stochastic optimizatino algorithms for deep AUC maximization. [Code is available soon] [Paper]

Acknowlewdgement and Disclaimer

This material is based upon work supported by the National Science Foundation under Grant No. 1844403. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.