Huber Loss

Huber Loss is a loss function that combines the properties of mean squared error and mean absolute error by being quadratic for small residuals and linear for large residuals to reduce sensitivity to outliers.

110

Publications

33.7K

Citations

278

Authors

150

Institutions

Publications per year

2017–2026

76

Variants

Huber Loss Function

Authors

278

  1. DG
    Deepak Gupta

    National Institute of Technology Arunachal Pradesh, India

    4 publications · 189 citations

    4

    h-index

  2. JF
    Jianqing Fan

    Princeton University, United States

    4 publications · 335 citations

    4

    h-index

  3. QL
    Quefeng Li

    University of North Carolina at Chapel Hill, United States

    2 publications · 242 citations

    2

    h-index

  4. LC
    Liangliang Cao

    University of Illinois Urbana-Champaign, United States

    2 publications · 175 citations

    2

    h-index

  5. MG
    Michael Gygli

    ETH Zurich, Switzerland

    2 publications · 175 citations

    2

    h-index

1–5 of 278

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 466 citations

    9

    h-index

  2. Neural Computing and Applications

    4 publications · 187 citations

    4

    h-index

  3. Journal of Complexity

    2 publications · 86 citations

    3

    h-index

  4. The Annals of Statistics

    3 publications · 29K citations

    3

    h-index

  5. Electronic Journal of Statistics

    2 publications · 43 citations

    2

    h-index

1–5