报告人：郭正初（浙江大学）

时  间：2025年4月29日09:00

地  点：海韵园实验楼S102

内容摘要:

In machine learning, it is commonly assumed that the training and test samples are drawn from the same underlying distribution. However, this assumption may  not always hold true in practice. In this talk, we delve into a scenario where the distribution of the input variables (also known as covariates), differs between the training and test phases. This situation is referred to as covariate shift. To address the challenges posed by covariate shift, various techniques have been developed, such as importance weighting, domain adaptation, and reweighting methods. In this talk, we specifically focus on the weighted spectral algorithm. Under mild conditions imposed on the weights, we demonstrate that this algorithm achieves satisfactory convergence rates. This talk is based on joint work with Prof. Jun Fan and Prof. Lei Shi.

个人简介：

郭正初，现为浙江大学数学科学学院教授，博士生导师，主要研究方向为学习理论和逼近论。主持浙江省杰出青年基金和国家基金面上项目，参与国家自然科学基金重点项目等，在Foundations of Computational Mathematics，Applied and Computational Harmonic Analysis, Journal of Machine Learning Research和Inverse Problems等期刊上发表论文多篇。

联系人：黄灿