Robust Loss

Robust Loss is a type of loss function in machine learning that is designed to minimize the influence of outliers and noisy data points by maintaining stable performance across deviations from model assumptions.

287

Publications

21.5K

Citations

867

Authors

364

Institutions

Publications per year

2017–2026

199

Authors

867

  1. YT
    Yingjie Tian

    Chinese Academy of Sciences, China

    5 publications · 132 citations

    5

    h-index

  2. CD
    Chris Ding

    The University of Texas at Arlington, United States

    5 publications · 471 citations

    5

    h-index

  3. LY
    Liming Yang

    China Agricultural University, China

    4 publications · 111 citations

    4

    h-index

  4. SF
    Saiji Fu

    University of Chinese Academy of Sciences, China

    4 publications · 99 citations

    4

    h-index

  5. XP
    Xi Peng

    Sichuan University, China

    4 publications · 548 citations

    4

    h-index

1–5 of 867

Venues

  1. arXiv (Cornell University)

    Repository · 55 publications · 4.3K citations

    29

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 12 publications · 1.8K citations

    12

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 1.1K citations

    7

    h-index

  4. 5

    h-index

  5. IEEE Transactions on Image Processing

    5 publications · 276 citations

    5

    h-index

1–5