Interpolation Threshold
Interpolation Threshold is a phenomenon in statistical learning theory wherein the generalization error of a model exhibits a non-monotonic behavior as a function of model complexity, specifically transitioning from overfitting to interpolation and back due to the onset of double descent, marking the point beyond which increasing model capacity allows perfect fitting of training data without necessarily worsening test performance.
12
Publications
747
Citations
21
Authors
16
Institutions
Publications per year
2017–2026
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1–5 of 12
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Google (United States)
Mountain View, United States
1 h-index
2 publications
70 citations
Laboratoire de Physique de l'Ecole Normale Supérieure
Paris, France
1 h-index
1 publications
79 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
191 citations
Meta (Israel)
Tel Aviv, Israel
1 h-index
1 publications
16 citations

University of Genoa
Genoa, Italy
1 h-index
3 publications
66 citations

Centre National de la Recherche Scientifique
Paris, France
1 h-index
1 publications
79 citations
Chinese Academy of Meteorological Sciences
Beijing, China
1 h-index
1 publications
13 citations
Institute of Psychology, Chinese Academy of Sciences
Beijing, China
1 h-index
2 publications
24 citations
1–8 of 16
Repository · 5 publications · 384 citations
5
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JSTAT · 2 publications · 90 citations
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1 publications · 22 citations
1
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1 publications · 191 citations
1
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1 publications · 12 citations
1
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