Model Robustness Analysis

Model Robustness Analysis is a systematic methodological framework within sensitivity analysis that evaluates the stability and reliability of a model's outputs under perturbations or variations in its assumptions, parameters, or input data, thereby distinguishing it from general validation techniques by focusing specifically on the model's resilience to change.

387

Publications

23.9K

Citations

1.3K

Authors

516

Institutions

Publications per year

2017–2026

222

Authors

1.3K

  1. AL
    Aishan Liu

    Beihang University, China

    6 publications · 338 citations

    6

    h-index

  2. XL
    Xianglong Liu

    Beihang University, China

    6 publications · 338 citations

    6

    h-index

  3. CC
    Ching‐Shui Cheng

    University of California, Berkeley, United States

    5 publications · 274 citations

    5

    h-index

  4. TP
    Tianyu Pang

    Tsinghua University, China

    5 publications · 192 citations

    5

    h-index

  5. JZ
    Jun Zhu

    Tsinghua University, China

    5 publications · 192 citations

    5

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 44 publications · 2.6K citations

    25

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 12 publications · 1.3K citations

    12

    h-index

  3. Philosophy of Science

    7 publications · 468 citations

    7

    h-index

  4. Biometrika

    6 publications · 402 citations

    6

    h-index

  5. IEEE Transactions on Automatic Control

    5 publications · 590 citations

    5

    h-index

1–5