Regularization Paths is a mathematical construct in gradient-based optimization that represents the continuous trajectory of model parameters as a regularization parameter is varied, distinguishing itself by characterizing the evolution of solutions under varying penalty strengths.
50
Publications
59.7K
Citations
96
Authors
60
Institutions
Publications per year
2017–2026
15
Variants
Regularization Path
Parents
Publications
50
1–5 of 50
Authors
96
- TH
8
h-index
- MJ
4
h-index
- SR
3
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- RT
3
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- RT
3
h-index
1–5 of 96
Institutions
60
Stanford University
Stanford, United States
8 h-index
17 publications
70.6K citations
University of California, Berkeley
Berkeley, United States
3 h-index
7 publications
466 citations
Columbia University
New York, United States
3 h-index
7 publications
289 citations
Rice University
Houston, United States
3 h-index
9 publications
264 citations
Peking University
Beijing, China
2 h-index
5 publications
126 citations
École Polytechnique
Palaiseau, France
2 h-index
2 publications
46 citations
Nanjing University of Information Science and Technology
Nanjing, China
2 h-index
3 publications
503 citations
ETH Zurich
Zurich, Switzerland
2 h-index
2 publications
43 citations
1–8 of 60
Venues
- Journal of Statistical Software
JSS · 4 publications · 20.4K citations
4
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- IEEE Transactions on Neural Networks and Learning Systems
3 publications · 504 citations
3
h-index
- arXiv (Cornell University)
Repository · 3 publications · 115 citations
3
h-index
- IEEE Transactions on Neural Networks
3 publications · 92 citations
3
h-index
- The Annals of Statistics
3 publications · 150 citations
3
h-index
1–5