Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers

Arnab Auddy, Haolin Zou, Kamiar Rahnamarad, Arian Maleki
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:2377-2385, 2024.

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.

Cite this Paper

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. } }
Endnote
%0 Conference Paper %T Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers %A Arnab Auddy %A Haolin Zou %A Kamiar Rahnamarad %A Arian Maleki %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-auddy24a %I PMLR %P 2377--2385 %U https://proceedings.mlr.press/v238/auddy24a.html %V 238 %X 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.
APA
Auddy, A., Zou, H., Rahnamarad, K. & Maleki, A.. (2024). Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:2377-2385 Available from https://proceedings.mlr.press/v238/auddy24a.html.

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