Jonas Wallin
Senior lecturer, Director of third cycle studies, Department of Statistics
The Hessian Screening Rule
Author
Editor
- S. Koyejo
- S. Mohamed
- A. Agarwal
- D. Belgrave
- K. Cho
- A. Oh
Summary, in English
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 `1-regularized least-squares (the lasso) and logistic regression. It also performs best in general on the real data sets that we examine.
Department/s
- Department of Statistics
- Lund University
Publishing year
2022-12-06
Language
English
Pages
25404-25421
Publication/Series
Advances in Neural Information Processing Systems
Volume
35
Document type
Conference paper
Publisher
Curran Associates, Inc
Topic
- Probability Theory and Statistics
Conference name
36th Conference on Neural Information Processing Systems, NeurIPS 2022
Conference date
2022-11-28 - 2022-12-09
Conference place
New Orleans, United States
Status
Published
Project
- Optimization and Algorithms for Sparse Regression
ISBN/ISSN/Other
- ISSN: 1049-5258
- ISBN: 9781713871088