Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning
We employ random matrix theory to establish consistency of generalized cross validation
(GCV) for estimating prediction risks of sketched ridge regression ensembles, enabling …
(GCV) for estimating prediction risks of sketched ridge regression ensembles, enabling …
Precise asymptotics of bagging regularized m-estimators
We characterize the squared prediction risk of ensemble estimators obtained through
subagging (subsample bootstrap aggregating) regularized M-estimators and construct a …
subagging (subsample bootstrap aggregating) regularized M-estimators and construct a …
Optimal Ridge Regularization for Out-of-Distribution Prediction
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 …
distribution prediction, where the test distribution deviates arbitrarily from the train …
Asymptotics of resampling without replacement in robust and logistic regression
PC Bellec, T Koriyama - arXiv preprint arXiv:2404.02070, 2024 - arxiv.org
This paper studies the asymptotics of resampling without replacement in the proportional
regime where dimension $ p $ and sample size $ n $ are of the same order. For a given …
regime where dimension $ p $ and sample size $ n $ are of the same order. For a given …
The Lasso error is bounded iff its active set size is bounded away from n in the proportional regime
PC Bellec - arXiv preprint arXiv:2501.02601, 2025 - arxiv.org
This note develops an analysis of the Lasso\(\hat b\) in linear models without any sparsity or
L1 assumption on the true regression vector, in the proportional regime where …
L1 assumption on the true regression vector, in the proportional regime where …