Robust Loss Functions

Robust Loss Functions is a class of loss functions in statistical learning and optimization that belongs to the broader category of objective functions used for model training, distinguished by its reduced sensitivity to outliers through the incorporation of mechanisms that downweight or bound the influence of large residuals.

778

Publications

55.5K

Citations

2K

Authors

692

Institutions

Publications per year

2017–2026

480

Authors

2K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    9 publications · 546 citations

    10

    h-index

  2. YT
    Yingjie Tian

    Chinese Academy of Sciences, China

    8 publications · 351 citations

    8

    h-index

  3. LY
    Liming Yang

    China Agricultural University, China

    7 publications · 149 citations

    7

    h-index

  4. SW
    Shiyuan Wang

    Southwest University, China

    6 publications · 290 citations

    6

    h-index

  5. XL
    Xuelong Li

    Northwestern Polytechnical University, China

    5 publications · 277 citations

    6

    h-index

1–5 of 2K

Venues

  1. arXiv (Cornell University)

    Repository · 117 publications · 11.2K citations

    42

    h-index

  2. 17

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 17 publications · 1.7K citations

    16

    h-index

  4. Neurocomputing

    14 publications · 1.3K citations

    14

    h-index

  5. Pattern Recognition

    12 publications · 739 citations

    12

    h-index

1–5