2 citations · 3 across the 2 of their papers we have counts for
5 papers
Error Estimation for Sketched SVD via the Bootstrap
Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney
In order to compute fast approximations to the singular value decompositions (SVD) of very large matrices, randomized sketching algorithms have become a leading approach. However,…
Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching
Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney
Although the operator (spectral) norm is one of the most widely used metrics for covariance estimation, comparatively little is known about the fluctuations of error in this norm.…
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting
Miles E. Lopes, Suofei Wu, Thomas C. M. Lee
When randomized ensemble methods such as bagging and random forests are implemented, a basic question arises: Is the ensemble large enough? In particular, the practitioner desires…
Estimating the Algorithmic Variance of Randomized Ensembles via the Bootstrap
Miles E. Lopes
Although the methods of bagging and random forests are some of the most widely used prediction methods, relatively little is known about their algorithmic convergence. In particula…
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
Miles E. Lopes
We study the residual bootstrap (RB) method in the context of high-dimensional linear regression. Specifically, we analyze the distributional approximation of linear contrasts $c^{…