报告人：陈洪教授

报告时间：2026年11月6日上午10:30-12:30

报告地点：腾讯会议：895-278-082

报告摘要：The manifold assumption states that high-dimensional ambient data usually possess an instinct geometric structure with a low-dimensional representation, which has promoted many manifold learning models achieving promising performance for wide applications. However, it is challenging to estimate the latent manifold when facing redundant and noisy input variables. To address the above issue, this report will introduce our preliminary explorations of bilevel optimization for additive models with manifold regularization and manifold fitting with Cycle-GAN. Generalization analysis and empirical evaluations are stated for the proposed bilevel learning schemes.

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。主持国家级项目8项,其中国家自然科学基金面上项目4项。在人工智能顶会NeurIPS、ICML、ICLR和数学与信息知名期刊ACHA、JAT、IEEE TPAMI/TIT/TIP/TNNLS/TCYB等发表系列研究论文,在Nature Genetics等合作发表智慧农业应用论文。

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