We consider linear regression problems with a varying number of random projections,
where we provably exhibit a double descent curve for a fixed prediction problem, with a high …

We establish precise structural and risk equivalences between subsampling and ridge
regularization for ensemble ridge estimators. Specifically, we prove that linear and quadratic …

Bagging is an important technique for stabilizing machine learning models. In this paper, we
derive a finite-sample guarantee on the stability of bagging for any model. Our result places …

While effective in practice, iterative methods for solving large systems of linear equations
can be significantly affected by problem-dependent condition number quantities. This makes …

We employ random matrix theory to establish consistency of generalized cross validation
(GCV) for estimating prediction risks of sketched ridge regression ensembles, enabling …

We study the behavior of optimal ridge regularization and optimal ridge risk for out-of-
distribution prediction, where the test distribution deviates arbitrarily from the train …

Bagging is an important technique for stabilizing machine learning models. In this paper, we
derive a finite-sample guarantee on the stability of bagging for any model. Our result places …

Matrix sketching is a powerful tool for reducing the size of large data matrices. Yet there are
fundamental limitations to this size reduction when we want to recover an accurate estimator …

Overparametrized Regression: Ridgeless Interpolation Page 1 Overparametrized
Regression: Ridgeless Interpolation Advanced Topics in Statistical Learning, Spring 2024 …

A Complete Bibliography of Publications in the SIAM Journal on Mathematics of Data Science
Page 1 A Complete Bibliography of Publications in the SIAM Journal on Mathematics of Data …