报告题目 Title：On Algorithmic Stability and Robustness of Bootstrap SGD

报告人 Speaker：Andreas Christmann

报告人所在单位 Affiliation：University of Bayreuth

时间 Time：2026-05-14 16:00-17:00

地点 Venue：Room 2001, Guanghua Eastern Main Tower(Handan Campus)

报告摘要 Abstract：The bootstrap is a computer-based resampling method that can provide good approximations to the finite sample distribution of a given statistic. In this talk some methods to use the empirical bootstrap approach for stochastic gradient descent (SGD) to minimize the empirical risk over a Hilbert space are investigated from the view point of algorithmic stability and statistical robustness. Two types of approaches are based on averages and are investigated from a theoretical point of view. Another type of bootstrap SGD is proposed to demonstrate that it is possible to construct purely distribution-free pointwise confidence intervals and distribution-free pointwise tolerance intervals of the conditional median function using bootstrap SGD.

个人简介 Bio：He got his dissertation and habilitation at the University of Dortmund (Germany). After positions as a visiting professor at KU Leuven (Belgium) and as professor at universities in Dortmund (Germany) and Brussels (VUB, Belgium). He is serving as Full Professor and Chair of Stochastics and Machine Learning at the University of Bayreuth (Germany) since 2008. Together with Prof. Steve Smale, Prof. Ding-Xuan Zhou, and Prof. Kurt Jetter, he organized the Oberwolfach workshop Learning Theory and Approximation, July 3-9, 2016. He was Action Editor of Journal of Machine Learning Research (JMLR) from 2013 to 2019 and since 2020 he is a member of the JMLR Editorial board of reviewers. Together with Prof. Ingo Steinwart he published a Springer book on Support Vector Machines. His main research topics are statistical learning theory and robust statistics.

海报 Poster: Andreas Christmann 学术报告.jpg