Haoyue BAI

I am a Ph.D. student at the Computer Sciences Department, University of Wisconsin-Madison, working with Prof. Robert Nowak. Prior to that, I obtained my bachelor's degree at Zhejiang University and my master's degree at The Hong Kong University of Science and Technology, supervised by Prof. S.-H. Gary Chan.

My research focuses on trustworthy AI and developing reliable machine learning in the open-world, with an emphasis on enhancing robustness against distribution shifts and detecting out-of-distribution data. Currently, I am interested in understanding and addressing safety issues in foundation models. Feel free to contact me for discussion.

Contact: Office 3160, Discovery Building, UW-Madison

Email  /  Google Scholar  /  Github  /  Twitter  /  Linkedin

* Received $5000 grant in API credits from OpenAI Researcher Access Program for supporting my research on AI Safety.

* Thanks OpenAI Superalignment Grants (acceptance rate about 1%-2%) for supporting my research on frontier topics of foundation models!

* I joined NEC Laboratories America as a Research Scientist Intern for the summer 2024.

Our approach strategically labels examples within a novel maximum disambiguation region, where the number of semantic and covariate OOD data roughly equalizes. By labeling within this region, we can maximally disambiguate the two types of OOD data, thereby maximizing the utility of the fixed labeling budget.

This paper provably learns domain-invariant representations in a hyperspherical space for OOD generalization.

This paper bridges the gap between OOD generalization and OOD detection in one coherent framework.

This benchmark may serve as a strong foothold that can be resorted to by future OoD generalization research.

This work takes the first step to understand the OoD generalization of neural network architectures systematically.

This work designs a general OoD generalization framework to tackle possible correlation shift and diversity shift in the real world.

This paper provides an up-to-date review of recent crowd counting approaches, and educate new researchers in this field the design principles and trade-offs.

2022.9 -- 2023.12. University of Wisconsin-Madison, Madison.
Research Assistant.
Advisor: Prof. Sharon Yixuan Li.

2021.6 -- 2021.9. The Chinese University of Hong Kong, Hong Kong.
Research Assistant.
Advisor: Prof. Bolei Zhou.

2017.7 -- 2017.8. The Hong Kong University, Hong Kong.
Summer Intern.
Advisor: Prof. Lucas C.K. Hui.

OpenAI Superalignment Grants (Acceptance rate no more than 2%)
Research Travel Grant, The Hong Kong University of Science and Technology
Outstanding Graduates, Zhejiang University
Alibaba-Zhejiang News Scholarship
First-Class Academic Scholarship, Zhejiang University (Top 3%)
First-Class Scholarship for Outstanding Students, Zhejiang University (Top 3%)
Outstanding Student Leader Awards, Zhejiang University (Top 6%)

Internation Conference on Machine Learning (ICML) 2023, 2024.
International Conference on Learning Representations (ICLR) 2023, 2024.
Conference on Neural Information Processing Systems (NeurIPS) 2023, 2024.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022, 2023, 2024.
European Conference on Computer Vision (ECCV) 2022, 2024.
International Conference on Computer Vision (ICCV) 2023.
AAAI Conference on Artificial Intelligence (AAAI) 2022.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2024.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2024.

University of Wisconsin–Madison
COMP CS400: Data Science Programming III (Spring 2024)

University of Wisconsin–Madison
COMP CS220: Data Science Programming I (Fall 2022)

The Hong Kong University of Science and Technology
COMP2611: Computer Organization (Fall 2019, Spring 2019)

Thanks to Jon Barron for website template.