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info.json
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{
"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.",
"authors": [
"Hussein Hazimeh",
"Rahul Mazumder",
"Tim Nonet"
],
"emails": [
"hazimeh@google.com",
"rahulmaz@mit.edu",
"tim.nonet@gmail.com"
],
"extra_links": [
[
"code",
"https://github.com/hazimehh/L0Learn"
]
],
"id": "22-0189",
"issue": 205,
"pages": [
1,
8
],
"special_issue": "MLOSS",
"title": "L0Learn: A Scalable Package for Sparse Learning using L0 Regularization",
"volume": 24,
"year": 2023
}