这是indexloc提供的服务，不要输入任何密码
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Machine Learning

arXiv:2202.04820 (cs)
[Submitted on 10 Feb 2022 (v1), last revised 9 Jun 2023 (this version, v2)]

Title:L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

Authors:Hussein Hazimeh, Rahul Mazumder, Tim Nonet
View PDF
Abstract:We present L0Learn: an open-source package for sparse linear regression and classification using $\ell_0$ regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combinatorial optimization. The package is built using C++ and has user-friendly R and Python interfaces. L0Learn can address problems with millions of features, achieving competitive run times and statistical performance with state-of-the-art sparse learning packages. L0Learn is available on both CRAN and GitHub (this https URL and this https URL).
Comments: Accepted to JMLR (MLOSS)
Subjects: Machine Learning (cs.LG); Mathematical Software (cs.MS); Computation (stat.CO); Machine Learning (stat.ML)
Cite as: arXiv:2202.04820 [cs.LG]
  (or arXiv:2202.04820v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2202.04820

Submission history

From: Hussein Hazimeh [view email]
[v1] Thu, 10 Feb 2022 03:51:25 UTC (55 KB)
[v2] Fri, 9 Jun 2023 16:20:37 UTC (58 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2022-02
Change to browse by:
cs
cs.MS
stat
stat.CO
stat.ML

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)