Univariate-Guided Interaction Modeling

Fuente: arXiv
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Main Authors: Echarghaoui, Aymen, Tibshirani, Robert
Format: Preprint
Published: 2025
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author Echarghaoui, Aymen
Tibshirani, Robert
author_facet Echarghaoui, Aymen
Tibshirani, Robert
contents We propose a procedure for sparse regression with pairwise interactions, by generalizing the Univariate Guided Sparse Regression (UniLasso) methodology. A central contribution is our introduction of a concept of univariate (or marginal) interactions. Using this concept, we propose two algorithms -- uniPairs and uniPairs-2stage -- , and evaluate their performance against established methods, including Glinternet and Sprinter. We show that our framework yields sparser models with more interpretable interactions. We also prove support recovery results for our proposal under suitable conditions.
format Preprint
id arxiv_https___arxiv_org_abs_2512_14413
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Univariate-Guided Interaction Modeling
Echarghaoui, Aymen
Tibshirani, Robert
Methodology
62J07
We propose a procedure for sparse regression with pairwise interactions, by generalizing the Univariate Guided Sparse Regression (UniLasso) methodology. A central contribution is our introduction of a concept of univariate (or marginal) interactions. Using this concept, we propose two algorithms -- uniPairs and uniPairs-2stage -- , and evaluate their performance against established methods, including Glinternet and Sprinter. We show that our framework yields sparser models with more interpretable interactions. We also prove support recovery results for our proposal under suitable conditions.
title Univariate-Guided Interaction Modeling
topic Methodology
62J07
url https://arxiv.org/abs/2512.14413