Calibration Loss

Calibration Loss is a type of loss function used in machine learning that quantifies the discrepancy between predicted probabilities and empirical outcomes to ensure that the model's confidence estimates are statistically aligned with observed frequencies.

17

Publications

1.1K

Citations

61

Authors

25

Institutions

Publications per year

2017–2026

14

Authors

61

  1. JM
    Jishnu Mukhoti

    University of Oxford, United Kingdom

    1 publications · 87 citations

    1

    h-index

  2. PK
    Puneet K. Dokania

    University of Oxford, United Kingdom

    1 publications · 87 citations

    1

    h-index

  3. YL
    Yue Liu

    National University of Defense Technology, China

    1 publications · 117 citations

    1

    h-index

  4. YZ
    Yu Zhang

    Huazhong University of Science and Technology, China

    1 publications · 29 citations

    1

    h-index

  5. BS
    Brian S. Bull

    Loma Linda University, United States

    1 publications · 10 citations

    1

    h-index

1–5 of 61

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 145 citations

    4

    h-index

  2. 3

    h-index

  3. Biometrika

    1 publications · 81 citations

    1

    h-index

  4. Oxford University Research Archive (ORA) (University of Oxford)

    Repository · 1 publications · 162 citations

    1

    h-index

  5. Journal of Digital Imaging

    1 publications · 71 citations

    1

    h-index

1–5