题目: Bilevel Additive Models

讲座专家：陈洪

时间：2024,4,18 19:00-21:00

腾讯会议：583-164-902

摘要: As an important paradigm in statistical machine learning, additive models often exhibit excellent capabilities on function approximation and variable selection. This report will explore the construction and algorithmic implementation of bilevel additive models, with focus on three issues including: (1) How to realize the data-driven structure discovery of variable groups? (2) How to automatically design the appropriate loss function? (3) How to mitigate the impact of noisy features on manifold learning? In theory, the report analyzes the upper bounds of generalization error and the consistency of variable selection. In applications, the effectiveness of bilevel additive models has been validated through data experiments.

报告人简介：陈洪，华中农业大学教授，博士生导师。研究方向为机器学习，人工智能的数学模型与算法。 主持国家级项目6项，其中面上项目3项，在人工智能顶会NeurIPS、ICML、ICLR等发表论文22篇, 在ACHA、JAT、IEEE TPAMI/TIP/TNNLS/TCYB、Neural Computation、Neural Networks、Pattern Recognition等应用数学与信息主流期刊发表论文40余篇。