3 papers
math.ST2022
Early stopping for -boosting in high-dimensional linear models
Bernhard Stankewitz
Increasingly high-dimensional data sets require that estimation methods do not only satisfy statistical guarantees but also remain computationally feasible. In this context, we con…
stat.ML2021
From inexact optimization to learning via gradient concentration
Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco
Optimization in machine learning typically deals with the minimization of empirical objectives defined by training data. However, the ultimate goal of learning is to minimize the e…
math.ST2019
Smoothed residual stopping for statistical inverse problems via truncated SVD estimation
Bernhard Stankewitz
This work examines under what circumstances adaptivity for truncated SVD estimation can be achieved by an early stopping rule based on the smoothed residuals $ \| ( A A^{\top} )^{α…