The Hessian Screening Rule
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Abstract (arXiv)
Predictor screening rules, which discard predictors before fitting a model, have had considerable impact on the speed with which sparse regression problems, such as the lasso, can be solved. In this paper we present a new screening rule for solving the lasso path: the Hessian Screening Rule. The rule uses second-order information from the model to provide both effective screening, particularly in the case of high correlation, as well as accurate warm starts. The proposed rule outperforms all alternatives we study on simulated data sets with both low and high correlation for ℓ1-regularized least-squares (the lasso) and logistic regression. It also performs best in general on the real data sets that we examine.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 73 references that are in the map. First posted on arXiv 27 Apr 2021.
Builds on
9 of 73 references.
References · 9
- 2016Gap Safe screening rules for sparsity enforcing penaltiescited by 39
- 2014SLOPE - Adaptive variable selection via convex optimizationcited by 33
- 2015Mind the duality gap: safer rules for the Lassocited by 33
- 2018Celer: a Fast Solver for the Lasso with Dual Extrapolationcited by 31
- 2013A Safe Screening Rule for Sparse Logistic Regressioncited by 14
- 2016Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regressioncited by 6
- 2021Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squarescited by 4
- 2017Hybrid safe-strong rules for efficient optimization in lasso-type problemscited by 2
- 2017ExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Modelscited by 2
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1.
3 papers.