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

Publications

265.2K

Citations

7.6K

Authors

2.3K

Institutions

Publications per year

2017–2026

452

Authors

7.6K

  1. RR
    Rand R. Wilcox

    University of Southern California, United States

    11 publications · 252 citations

    11

    h-index

  2. LV
    L.D. Van Vleck

    Cornell University, United States

    11 publications · 341 citations

    11

    h-index

  3. HJ
    H. J. Keselman

    University of Manitoba, Canada

    11 publications · 551 citations

    11

    h-index

  4. TB
    T. B. Whitaker

    North Carolina State University, United States

    10 publications · 328 citations

    10

    h-index

  5. ZD
    Zili Deng

    Heilongjiang University, China

    9 publications · 373 citations

    9

    h-index

1–5 of 7.6K

Venues

  1. Journal of the American Statistical Association

    142 publications · 14K citations

    54

    h-index

  2. Biometrics

    62 publications · 7.3K citations

    35

    h-index

  3. Biometrika

    57 publications · 11K citations

    32

    h-index

  4. The Annals of Mathematical Statistics

    36 publications · 6.6K citations

    26

    h-index

  5. Technometrics

    46 publications · 3K citations

    25

    h-index

1–5