I am an Assistant Professor at Northern Illinois University. I got my Ph.D. degree at Texas A&M University, advised by
Prof. Tianbao Yang.
Here is my
[Google Scholar Profile].
Funded positions avaiable for PhD/Master/Undergraduate. Interested students
please send me an email with your CV, graduate and/or undergraduate transcripts, as well as a writing sample if applicable.
Research Interest
- Federated Learning, Machine Learning, Optimization.
Preprints
- Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization
[Paper]
Zhishuai Guo, Quanqi Hu, Lijun Zhang, Tianbao Yang
arXiv preprint arXiv:2105.02266
Publications
- Communication-Efficient Federated Group Distributionally Robust Optimization
Zhishuai Guo, Tianbao Yang
arXiv preprint arXiv:2104.14840
Accepted to NeurIPS 2024. To appear.
- On Stochastic Moving-Average Estimators for Non-Convex Optimization
[Paper]
Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
arXiv preprint arXiv:2104.14840
Accepted to Machine Learning Journal. To appear.
- FeDXL: Provable Federated Learning for Deep X-Risk Optimization
[Paper]
Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang
International Conference on Machine Learning (ICML), 2023
- Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
[Paper]
Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang
International Conference on Machine Learning (ICML), 2023
- Fast Objective and Duality Gap Convergence for Nonconvex-Strongly-Concave Min-Max Problems with PL Condition
[Paper]
Zhishuai Guo, Yan Yan, Zhuoning Yuan, Tianbao Yang
Journal of Machine Learning Research.
- Compositional Training for End-to-End Deep AUC Maximization
[Paper]
Zhuoning Yuan, Zhishuai Guo, Nitesh Chawla, Tianbao Yang
In International Conference on Learning Representations (ICLR), 2022 (Spotlight)
- A Novel Convergence Analysis for Algorithms of the Adam Family
[Short Version]
[Full Version]
Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
In NeurIPS Workshop on Optimization for Machine Learning (OPT), 2021
- An Online Method for Deep Distributionally Robust Optimization
[Paper]
Qi Qi*, Zhishuai Guo*, Yi Xu, Rong Jin, Tianbao Yang (*Equal Contribution)
In Advances in Neural Information Processing Systems (NeurIPS), 2021
- Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
[Paper]
[Code]
Zhuoning Yuan*, Zhishuai Guo*, Yi Xu, Yiming Ying, Tianbao Yang (*Equal Contribution)
In International Conference on Machine Learning (ICML), 2021
- Accelerating Deep Learning with Millions of Classes
[Paper]
Zhuoning Yuan, Zhishuai Guo, Xiaotian Yu, Xiaoyu Wang, Tianbao Yang
In European Conference on Computer Vision (ECCV), 2020
- Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
[Paper]
[Code]
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
In International Conference on Machine Learning (ICML), 2020
- A New Local Density for Density Peak Clustering
[Paper]
Zhishuai Guo, Tianyi Huang, Zhiling Cai, William Zhu
In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018
Education
- Ph.D. in Computer Science, Texas A&M University
- M.S. in Computer Science, The University of Iowa
- B.S. in Computer Science, University of Electronic Science and Technology of China
Teaching
- CSCI:644: Applied Machine Learning, Fall 2024.
Teaching Assistant
Industry Experience
- Meta, Software Engineer Intern, June 2022 - Aug 2022.
- Amazon, Applied Scientist Intern, June 2021 - Aug 2021.