Variances
Variances is a theoretical construct in machine learning that characterizes the sensitivity of a model's predictions to variations in the training data, distinguishing it from bias by quantifying the extent to which the model's estimates fluctuate across different samples.
3.4K
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Citations
7.6K
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Institutions
Publications per year
2017–2026
452
Variants
Variance
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University of Michigan
Ann Arbor, United States
21 h-index
47 publications
3K citations

Cornell University
Ithaca, United States
21 h-index
60 publications
3K citations
Harvard University
Cambridge, United States
20 h-index
38 publications
6K citations

University of Minnesota
Minneapolis, United States
20 h-index
44 publications
6.1K citations
North Carolina State University
Raleigh, United States
19 h-index
66 publications
2.1K citations

University of Toronto
Toronto, Canada
19 h-index
45 publications
4.8K citations
University of Wisconsin–Madison
Madison, United States
19 h-index
48 publications
2.2K citations
Stanford University
Stanford, United States
19 h-index
33 publications
7.2K citations
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142 publications · 14K citations
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62 publications · 7.3K citations
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57 publications · 11K citations
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36 publications · 6.6K citations
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46 publications · 3K citations
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