High-dimensional Overfitting
High-Dimensional Overfitting is a type of model overfitting that occurs when a learning algorithm captures noise or spurious correlations in high-dimensional feature spaces, leading to poor generalization despite good fit on training data.
66
Publications
7.5K
Citations
218
Authors
113
Institutions
Publications per year
2017–2026
46
Parents
66
Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer et al. · Econometrics Journal · 2017 · 2.5K citations · Full text
Shaoxia Chen, Greg McMullan, A.R. Faruqi et al. · Ultramicroscopy · 2013 · 1.1K citations · Full text
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113
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
8 publications
11K citations
Harvard University
Cambridge, United States
3 h-index
7 publications
2.8K citations
Nanyang Technological University
Singapore, Singapore
3 h-index
6 publications
106 citations

Brigham and Women's Hospital
Boston, United States
2 h-index
6 publications
333 citations
Google (United States)
Mountain View, United States
2 h-index
3 publications
100 citations

Victoria University of Wellington
Wellington, New Zealand
2 h-index
6 publications
747 citations
National Cancer Institute
Bethesda, United States
2 h-index
3 publications
294 citations

Yokohama National University
Yokohama, Japan
2 h-index
2 publications
254 citations
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Repository · 6 publications · 274 citations
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TKDE · 2 publications · 35 citations
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Conference · 1 publications · 175 citations
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2 publications · 88 citations
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1 publications · 23 citations
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