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Found 4 papers, 1 papers with code
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
However, the user does not know how much error is created by the randomization, which can be detrimental in two ways: On one hand, the user may try to manage the unknown error with theoretical worst-case error bounds, but this approach is impractical when the bounds involve unknown constants, and it typically leads to excessive computation.
Error Estimation for Sketched SVD
In order to compute fast approximations to the singular value decompositions (SVD) of very large matrices, randomized sketching algorithms have become a leading approach.
Bootstrapping spectral statistics in high dimensions
Spectral statistics play a central role in many multivariate testing problems.
Methodology Primary: 62F40, 60B20, Secondary: 62H10, 60F05
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
We study the residual bootstrap (RB) method in the context of high-dimensional linear regression.
