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Can we globally optimize cross-validation loss? Quasiconvexity in ridge
  regression

Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression

19 July 2021
William T. Stephenson
Zachary Frangella
Madeleine Udell
Tamara Broderick
ArXivPDFHTML

Papers citing "Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression"

2 / 2 papers shown
Title
Stability Regularized Cross-Validation
Stability Regularized Cross-Validation
Ryan Cory-Wright
A. Gómez
26
0
0
11 May 2025
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data
Angus Dempster
Geoffrey I. Webb
Daniel F. Schmidt
29
0
0
28 Jan 2024
1