Ranking Loss

Ranking Loss is a type of loss function used in machine learning that penalizes incorrect relative ordering of predicted scores by enforcing a margin between the scores of positive and negative instances in ranking tasks.

289

Publications

23.7K

Citations

988

Authors

293

Institutions

Publications per year

2017–2026

203

Variants

Ranking Losses

Authors

988

  1. XW
    Xuanhui Wang

    Google (United States), United States

    5 publications · 251 citations

    5

    h-index

  2. ZZ
    Zhi‐Hua Zhou

    Nanjing University, China

    5 publications · 248 citations

    5

    h-index

  3. MB
    Michael Bendersky

    Google (United States), United States

    5 publications · 251 citations

    5

    h-index

  4. MN
    Marc Najork

    Google (United States), United States

    4 publications · 185 citations

    4

    h-index

  5. SA
    Shivani Agarwal

    Indian Institute of Science Bangalore, India

    4 publications · 100 citations

    4

    h-index

1–5 of 988

Venues

  1. arXiv (Cornell University)

    Repository · 41 publications · 2.6K citations

    24

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 12 publications · 857 citations

    11

    h-index

  3. IEEE Transactions on Multimedia

    10 publications · 372 citations

    10

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 227 citations

    7

    h-index

  5. IEEE Transactions on Image Processing

    6 publications · 302 citations

    6

    h-index

1–5