Logistic Loss

Logistic Loss is a loss function in machine learning that quantifies the discrepancy between predicted probabilities and binary true labels by applying the logarithmic penalty derived from the logistic (sigmoid) transformation.

121

Publications

8.2K

Citations

328

Authors

162

Institutions

Publications per year

2017–2026

65

Authors

328

  1. MT
    Matus Telgarsky

    University of Illinois Urbana-Champaign, United States

    5 publications · 169 citations

    5

    h-index

  2. FB
    Francis Bach

    Institut national de recherche en sciences et technologies du numérique, France

    5 publications · 246 citations

    5

    h-index

  3. PM
    Philip M. Long

    National University of Singapore, Singapore

    4 publications · 106 citations

    4

    h-index

  4. CT
    Christos Thrampoulidis

    California Institute of Technology, United States

    4 publications · 128 citations

    4

    h-index

  5. DP
    David P. Helmbold

    University of California, Santa Cruz, United States

    4 publications · 123 citations

    4

    h-index

1–5 of 328

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 2.2K citations

    22

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 163 citations

    6

    h-index

  3. Conference on Learning Theory

    Conference · 3 publications · 50 citations

    3

    h-index

  4. IEEE Transactions on Image Processing

    3 publications · 181 citations

    3

    h-index

  5. International Conference on Machine Learning

    Conference · 3 publications · 347 citations

    3

    h-index

1–5