报告题目1：On aspects of localized learning

报告人：Professor Andreas Christmann, University of Bayreuth, Germany

报告时间：2024年5月14日（周二）14:30-15:30

报告地点：20-200

报告摘要：Many machine learning methods do not scale well with respect to computation time and computer memory, if the sample size increases. Divide and conquer methods can be helpful in this respect and often allow for parallel computing. This is true e.g. for distributed learning. This talk will focus on localized learning which is a related but slightly different approach. We will mainly consider aspects of statistical robustness and stability questions. The first part will deal with total stability of kernel-based methods and is based on Koehler and Christmann (2022, JMLR, 23, 1-41). The second part of the talk will investigate the question of qualitative robustness without specifying a particular learning method.

报告人简介：Andreas Christmann received a Ph.D degree from University of Dortmund, Germany. After positions as professor at KUL in Leuven and at VUB in Brussels (both in Belgium), he is a Full Professor and Chair for Stochastics and Machine Learning in the Department of Mathematics, University of Bayreuth, Germany. His research interests include statistical learning theory, kernel methods, robust statistics, and statistical methods for big data. He has published many research papers and 1 monograph entitled “Support Vector Machine” in these areas. He is an Action Editor for “Journal of  Machine Learning Research” from 2013 to 2019.  Since 2020, he is the member of the JMLR Editorial board of reviewers.

报告题目2：Online Learning in Reproducing Kernel Hilbert Space

报告人：郭正初教授，浙江大学

报告时间：2024年5月14日（周二）15:30-16:30

报告地点：20-200

报告摘要：Analyzing and processing large-scale data sets is becoming ubiquitous in the era of big data. Online learning has attracted increasing interest in recent years due to its low computational complexity and storage requirements, it has been applied to various learning tasks. In this talk, we will present some results of online learning algorithms in Reproducing Kernel Hilbert space. This talk is based on the joint work with Prof. Lei Shi, Prof. Andreas Christmann, and Prof. Xin Guo.

报告人简介：郭正初，理学博士，现为浙江大学数学科学学院教授，博士生导师，主要研究方向为统计学习理论。2011年于中山大学和香港城市大学取得博士学位，毕业后在香港城市大学和英国埃克塞特大学从事博士后研究工作，于2013年8月加入浙江大学数学科学学院，先后任特聘副研究员(2013-2016)，副教授(2017-2020)，教授(2021-至今)。现主持国家基金面上项目等，参与国家自然科学基金重点项目，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。

邀请人：向道红