15 citations · 49 across the 18 of their papers we have counts for
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Precise Asymptotics of Bagging Regularized M-estimators
Takuya Koriyama, Pratik Patil, Jin-Hong Du +2
We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent…
Optimal Ridge Regularization for Out-of-Distribution Prediction
Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
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 dis…
Corrected generalized cross-validation for finite ensembles of penalized estimators
Pierre C. Bellec, Jin-Hong Du, Takuya Koriyama +2
Generalized cross-validation (GCV) is a widely-used method for estimating the squared out-of-sample prediction risk that employs a scalar degrees of freedom adjustment (in a multip…
Generalized equivalences between subsampling and ridge regularization
Pratik Patil, Jin-Hong Du
We establish precise structural and risk equivalences between subsampling and ridge regularization for ensemble ridge estimators. Specifically, we prove that linear and quadratic f…
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Jin-Hong Du, Pratik Patil, Arun Kumar Kuchibhotla
We study subsampling-based ridge ensembles in the proportional asymptotics regime, where the feature size grows proportionally with the sample size such that their ratio converges…
Bagging in overparameterized learning: Risk characterization and risk monotonization
Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the performance of prediction procedures. In this paper, we study the prediction risk of…