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Jonas Wallin. Foto.

Jonas Wallin

Universitetslektor, Studierektor för forskarutbildningen, Statistiska institutionen

Jonas Wallin. Foto.

The Hessian Screening Rule

Författare

  • Johan Larsson
  • Jonas Wallin

Redaktör

  • 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.

Avdelning/ar

  • Statistiska institutionen
  • Lunds universitet

Publiceringsår

2022-12-06

Språk

Engelska

Sidor

25404-25421

Publikation/Tidskrift/Serie

Advances in Neural Information Processing Systems

Volym

35

Dokumenttyp

Konferensbidrag

Förlag

Curran Associates, Inc

Ämne

  • 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

Projekt

  • Optimization and Algorithms for Sparse Regression

ISBN/ISSN/Övrigt

  • ISSN: 1049-5258
  • ISBN: 9781713871088