报告题目：Statistical Learning Theory for Deep Functional Networks

报告人：樊军副教授，香港浸会大学

报告时间：2025年7月15日（周二）9:00-10:00

报告地点：20-200

报告摘要：Recently, there has been a significant increase in research aimed at understanding the theoretical foundations of neural networks defined in infinite-dimensional function spaces. While existing studies have explored various aspects of this topic, our understanding of the approximation and learning abilities of these networks remains limited. In this talk, I will present our recent work on the generalization analysis of deep functional networks designed for learning nonlinear mappings from function spaces to R (i.e., functionals). By investigating the convergence rates of approximation and generalization errors, we uncover important insights into the theoretical properties of these networks. This analysis not only deepens our understanding of deep functional networks but also paves the way for their effective application in areas such as operator learning, functional data analysis, and scientific machine learning.

报告人简介：樊军，香港浸会大学数学系副教授。樊军博士于2013年在香港城市大学获得博士学位，2017年入职香港浸会大学数学系，2022年获终身教职。他曾于威斯康辛大学麦迪逊分校担任博士后研究员。研究兴趣包括统计学习理论和深度学习理论。在机器学习领域中有影响力的期刊上发表了一系列学术论文，主要包括Journal of Machine Learning Research, Applied and Computational Harmonic Analysis, Neural Networks, Journal of Fourier Analysis and Applications等。现为Mathematical Foundations of Computing, Software Impacts, Journal of Mathematics, Frontiers in Applied Mathematics and Statistics的编委。

邀请人：向道红