Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer, Marion I. Menzel, Holger Rauhut
TL;DR使用不重复抽样改进去偏估计器以提高不确定性量化方法在傅里叶成像中的性能。
Abstract
Over the last few years, debiased estimators have been proposed in order to establish rigorous confidence intervals for high-dimensional problems in machine learning and data science. The core argument is that the error of these estimators with respect to the ground truth can be expres