Sparse Models
Sparse Models is a class of statistical models characterized by the deliberate restriction of model parameters to contain a significant number of zero values, thereby enhancing interpretability and computational efficiency in high-dimensional data settings.
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98.9K
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3.1K
Authors
1K
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Publications per year
2017–2026
475
Variants
Sparsity-based Models
Parents
1.2K
High dimensional graphs and variable selection with the Lasso
2006 · 2.7K citations
High-dimensional graphs and variable selection with the Lasso
The Annals of Statistics · 2006 · 2.5K citations · Full text
On Model Selection Consistency of Lasso
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Massachusetts Institute of Technology, United States
19 publications · 2.9K citations
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Massachusetts Institute of Technology
Cambridge, United States
24 h-index
78 publications
13.8K citations
Stanford University
Stanford, United States
24 h-index
76 publications
6.2K citations
University of California, Berkeley
Berkeley, United States
21 h-index
63 publications
8.7K citations
Princeton University
Princeton, United States
21 h-index
51 publications
5K citations
Carnegie Mellon University
Pittsburgh, United States
20 h-index
78 publications
5.5K citations

University of Minnesota
Minneapolis, United States
19 h-index
51 publications
3.9K citations

Centre National de la Recherche Scientifique
Paris, France
18 h-index
50 publications
9.2K citations
University of Michigan
Ann Arbor, United States
18 h-index
39 publications
6.8K citations
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Repository · 114 publications · 6.4K citations
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39 publications · 4.9K citations
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24 publications · 3.8K citations
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20 publications · 751 citations
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15 publications · 1.1K citations
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