Ensemble Stability

Ensemble Stability is a methodological framework in machine learning that quantifies the robustness of ensemble models by measuring the consistency of their predictive outputs under perturbations or variations in training data, model initialization, or submodel composition.

43

Publications

2.1K

Citations

130

Authors

87

Institutions

Publications per year

2017–2026

15

Authors

130

  1. HS
    Hao Su

    University of Electronic Science and Technology of China, China

    2 publications · 165 citations

    2

    h-index

  2. MP
    Massimiliano Pontil

    University College London, United Kingdom

    2 publications · 144 citations

    2

    h-index

  3. VM
    Vladimir M. Krasnopolsky

    NOAA National Centers for Environmental Prediction, United States

    2 publications · 45 citations

    2

    h-index

  4. XD
    Xin Dong

    Harvard University Press, United States

    2 publications · 165 citations

    2

    h-index

  5. SZ
    Shilin Zhu

    University of California San Diego, United States

    2 publications · 165 citations

    2

    h-index

1–5 of 130

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 25 citations

    2

    h-index

  2. Systems & Control Letters

    2 publications · 55 citations

    2

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 79 citations

    2

    h-index

  4. Current Biology

    1 publications · 26 citations

    1

    h-index

  5. IEEE Transactions on Automatic Control

    1 publications · 35 citations

    1

    h-index

1–5