【计算与应用讨论班】:On Algorithmic Stability and Robustness of Bootstrap SGD

Speaker：Andreas Christmann（University of Bayreuth, Germany）

Time：2026年05月21日（星期四），15:00

Place：海纳苑2幢204

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.

Contact Person:郭正初（guozc@zju.edu.cn）