Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers
Arnab Auddy, Haolin Zou, Kamiar Rahnamarad, Arian Maleki
Abstract
The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet computationally demanding method to estimate OO. Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistically reliable estimate of LO (and OO) for generalized linear models with twice differentiable regularizers. For problems involving non-differentiable regularizers, despite significant empirical evidence, the theoretical understanding of ALO’s error remains unknown. In this paper, we present a novel theory for a wide class of problems in the generalized linear model family with the non-differentiable $\ell_1$ regularizer. We bound the error \(|{\rm ALO}-{\rm LO}|\){in} terms of intuitive metrics such as the size of leave-\(i\)-out perturbations in active sets, sample size $n$, number of features $p$ and signal-to-noise ratio (SNR). As a consequence, for the $\ell_1$ regularized problems, we show that $|{\rm ALO}-{\rm LO}| \stackrel{p\rightarrow \infty}{\longrightarrow} 0$ while $n/p$ and SNR remain bounded.
BibTeX
@InProceedings{pmlr-v238-auddy24a,
title = {Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers},
author = {Auddy, Arnab and Zou, Haolin and Rahnamarad, Kamiar and Maleki, Arian},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {2377--2385},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/auddy24a/auddy24a.pdf},
url = {https://proceedings.mlr.press/v238/auddy24a.html},
abstract = {The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet computationally demanding method to estimate OO. Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistically reliable estimate of LO (and OO) for generalized linear models with twice differentiable regularizers. For problems involving non-differentiable regularizers, despite significant empirical evidence, the theoretical understanding of ALO’s error remains unknown. In this paper, we present a novel theory for a wide class of problems in the generalized linear model family with the non-differentiable $\ell_1$ regularizer. We bound the error \(|{\rm ALO}-{\rm LO}|\){in} terms of intuitive metrics such as the size of leave-\(i\)-out perturbations in active sets, sample size $n$, number of features $p$ and signal-to-noise ratio (SNR). As a consequence, for the $\ell_1$ regularized problems, we show that $|{\rm ALO}-{\rm LO}| \stackrel{p\rightarrow \infty}{\longrightarrow} 0$ while $n/p$ and SNR remain bounded.}
}