Invited Talk: Prof. Dr. Xiaolin Huang (Shanghai Jiao Tong University) – Machine Learning and Machine Unlearning in the View of Generalization, Jan 10th 2025, 10 AM CET
It’s a great pleasure to welcome Prof. Dr. Xiaolin Huang back at the Pattern Recognition Lab!
Title: Machine Learning and Machine Unlearning in the View of Generalization
Date: Jan 10th 2025, 10 AM CET
Location: https://fau.zoom-x.de/j/64518890066?pwd=XgXgVw4XGCtbUKk6jAk0AXBzBQcAg5.1
Abstract
Generalization is a critical challenge in machine learning, particularly for deep learning models, which often achieve high training accuracy but exhibit varying performance on new data. Our research focuses on improving the generalization capability of deep learning models and has revealed that training dynamics can be effectively captured within a low-dimensional space. This insight has led to advancements in training speed and generalization performance. Together with sharpness-aware minimization (SAM), another efficient method to enhance the generalization, we have successfully applied these approaches to training deep neural networks in industrial applications. Exploring generalization also contributes to the field of machine unlearning, an emerging and intriguing topic with both practical and theoretical implications.
Short Bio
Xiaolin Huang received his BS degree from Xi’an Jiaotong University, Xi’an, China, in 2006, and his PhD degree from Tsinghua University, Beijing, China. From 2012 to 2015, he worked as a postdoctoral researcher with ESAT-STADIUS, KU Leuven, Leuven, Belgium. After that he was selected as an Alexander von Humboldt fellow and working with Pattern Recognition Lab, the Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. In 2016, he joined the Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China, where he became a Full Professor in 2023.
His research focuses on machine learning and optimization, with a particular emphasis on generalization analysis through both practical and theoretical approaches. He has authored dozens of papers in top-tier journals and conferences, including JMLR, IEEE TPAMI, ACHA, NeurIPS, ICLR, CVPR, IEEE TMI, etc. Additionally, he has published a survey on piecewise linear neural networks in Nature Reviews Methods Premiers. Currently, he serves as Vice Dean of the Department of Automation at Shanghai Jiao Tong University, an Editor for Machine Learning, and an Area Chair for prestigious conferences such as ICLR, CVPR, ICCV, etc.
