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McKelvey School of Engineering

Brown School

Embracing Change: Tackling In-the-Wild Shifts in Machine Learning

Friday, March 10, 2023 | 11:00 AM

Jubel 121

Huaxiu Yao

Postdoctoral Scholar

Computer Science Department

Standford University 

The real-world deployment of machine learning algorithms often poses challenges due to shifts in data distributions and tasks. These shifts can lead to a degradation in model performance, as the model may not have seen such changes during training. It can also make it difficult for the model to generalize to new scenarios and can lead to poor performance in real-world applications. In this talk, I will present our research on building machine learning models that are unbiased, widely generalizable, and easily adaptable to different shifts. Specifically, I will first discuss our approach to learning unbiased models through selective augmentation for scenarios with subpopulation shifts. Second, I will also delve into the utilization of domain relational information to enhance model generalizability for arbitrary domain shifts. Then, I will present our techniques for quickly adapting models to new tasks with limited labeled data. Additionally, I will show our success practices for addressing shifts in real-world applications, such as in the healthcare, e-commerce, and transportation industries. The talk will also cover the remaining challenges and future research directions in this area.

Event Type

Seminar/Colloquia

Schools

McKelvey School of Engineering

Topic

Science & Technology

Department
Computer Science & Engineering
Event Contact

myrna@wustl.edu

Speaker Information

Huaxiu Yao is a Postdoctoral Scholar in Computer Science at Stanford University, working with

Prof. Chelsea Finn. Currently, his research interests focus on building machine learning models that are building machine learning models that are unbiased, widely generalizable, and easily adaptable to changing environments and tasks. He is also dedicated to applying these methods to solve real-world data science applications, such as healthcare, transportation, and online education. Huaxiu earned his Ph.D. degree from Pennsylvania State University. He has over 30 publications in leading machine learning and data science venues such as ICML, ICLR, and NeurIPS. He also has organized and co-organized workshops at ICML and NeurIPS, and has served as a tutorial speaker at conferences such as KDD, AAAI and IJCAI. Additionally, Huaxiu has extensive industry experience, having interned at companies such as Amazon Science, Salesforce Research. For more information, please visit https://huaxiuyao.mystrikingly.com/.

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