Beyond L1: Faster and Better Sparse Models with skglm

Quentin Bertrand; Quentin Klopfenstein; Pierre Antoine Bannier; Gauthier Gidel; Mathurin Massias

Beyond L1: Faster and Better Sparse Models with skglm

Quentin Bertrand
, Quentin Klopfenstein
, Pierre Antoine Bannier
, Gauthier Gidel
, Mathurin Massias

UdeM
University of Luxembourg
Canada CIFAR AI
Ecole Normale Supérieure de Lyon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddressed models, and is extensively shown to improve state-of-art algorithms. We release skglm, a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
Editors	S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
Publisher	Neural information processing systems foundation
ISBN (Electronic)	9781713871088
Publication status	Published - 1 Jan 2022
Externally published	Yes
Event	36th Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, United States Duration: 28 Nov 2022 → 9 Dec 2022

Publication series

Name	Advances in Neural Information Processing Systems
Volume	35
ISSN (Print)	1049-5258

Conference

Conference	36th Conference on Neural Information Processing Systems, NeurIPS 2022
Country/Territory	United States
City	New Orleans
Period	28/11/22 → 9/12/22

Cite this

Bertrand, Q., Klopfenstein, Q., Bannier, P. A., Gidel, G., & Massias, M. (2022). Beyond L1: Faster and Better Sparse Models with skglm. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022 (Advances in Neural Information Processing Systems; Vol. 35). Neural information processing systems foundation.

Bertrand, Quentin ; Klopfenstein, Quentin ; Bannier, Pierre Antoine et al. / Beyond L1 : Faster and Better Sparse Models with skglm. Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. editor / S. Koyejo ; S. Mohamed ; A. Agarwal ; D. Belgrave ; K. Cho ; A. Oh. Neural information processing systems foundation, 2022. (Advances in Neural Information Processing Systems).

@inproceedings{bf517a69bb2f40aebd2f9250b7c9f7eb,

title = "Beyond L1: Faster and Better Sparse Models with skglm",

abstract = "We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddressed models, and is extensively shown to improve state-of-art algorithms. We release skglm, a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties.",

author = "Quentin Bertrand and Quentin Klopfenstein and Bannier, \{Pierre Antoine\} and Gauthier Gidel and Mathurin Massias",

note = "Publisher Copyright: {\textcopyright} 2022 Neural information processing systems foundation. All rights reserved.; 36th Conference on Neural Information Processing Systems, NeurIPS 2022 ; Conference date: 28-11-2022 Through 09-12-2022",

year = "2022",

month = jan,

day = "1",

language = "English",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

editor = "S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh",

booktitle = "Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022",

}

Bertrand, Q, Klopfenstein, Q, Bannier, PA, Gidel, G & Massias, M 2022, Beyond L1: Faster and Better Sparse Models with skglm. in S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho & A Oh (eds), Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Advances in Neural Information Processing Systems, vol. 35, Neural information processing systems foundation, 36th Conference on Neural Information Processing Systems, NeurIPS 2022, New Orleans, United States, 28/11/22.

Beyond L1: Faster and Better Sparse Models with skglm. / Bertrand, Quentin; Klopfenstein, Quentin; Bannier, Pierre Antoine et al.
Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. ed. / S. Koyejo; S. Mohamed; A. Agarwal; D. Belgrave; K. Cho; A. Oh. Neural information processing systems foundation, 2022. (Advances in Neural Information Processing Systems; Vol. 35).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Beyond L1

T2 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022

AU - Bertrand, Quentin

AU - Klopfenstein, Quentin

AU - Bannier, Pierre Antoine

AU - Gidel, Gauthier

AU - Massias, Mathurin

PY - 2022/1/1

Y1 - 2022/1/1

N2 - We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddressed models, and is extensively shown to improve state-of-art algorithms. We release skglm, a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties.

AB - We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddressed models, and is extensively shown to improve state-of-art algorithms. We release skglm, a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties.

UR - https://www.scopus.com/pages/publications/85163146227

M3 - Conference contribution

AN - SCOPUS:85163146227

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022

A2 - Koyejo, S.

A2 - Mohamed, S.

A2 - Agarwal, A.

A2 - Belgrave, D.

A2 - Cho, K.

A2 - Oh, A.

PB - Neural information processing systems foundation

Y2 - 28 November 2022 through 9 December 2022

ER -

Bertrand Q, Klopfenstein Q, Bannier PA, Gidel G, Massias M. Beyond L1: Faster and Better Sparse Models with skglm. In Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, editors, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Neural information processing systems foundation. 2022. (Advances in Neural Information Processing Systems).

Beyond L1: Faster and Better Sparse Models with skglm

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