Interpretable Modeling

Interpretable Modeling is a type of mathematical modeling that, as a genus, produces models whose structures and parameters are transparent and comprehensible to human analysts, and, as a differentia, explicitly prioritizes interpretability to enable understanding, validation, and trust in the model's reasoning and predictions.

1.5K

Publications

157.9K

Citations

6.1K

Authors

1.8K

Institutions

Publications per year

2017–2026

1.3K

Variants

Explainable Modeling

Authors

6.1K

  1. CR
    Cynthia Rudin

    Duke University, United States

    16 publications · 9.7K citations

    14

    h-index

  2. RC
    Rich Caruana

    Cornell University, United States

    8 publications · 783 citations

    8

    h-index

  3. TW
    Tong Wang

    University of Iowa, United States

    7 publications · 212 citations

    7

    h-index

  4. CC
    Chaofan Chen

    Duke University, United States

    6 publications · 370 citations

    6

    h-index

  5. NM
    N. M. Anoop Krishnan

    Indian Institute of Technology Delhi, India

    5 publications · 375 citations

    5

    h-index

1–5 of 6.1K

Venues

  1. arXiv (Cornell University)

    Repository · 114 publications · 6K citations

    33

    h-index

  2. Scientific Reports

    21 publications · 718 citations

    18

    h-index

  3. Nature Communications

    16 publications · 1.2K citations

    15

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 21 publications · 480 citations

    14

    h-index

  5. IEEE Access

    15 publications · 657 citations

    14

    h-index

1–5