Interpolation Threshold

Interpolation Threshold is a phenomenon in statistical learning theory wherein the generalization error of a model exhibits a non-monotonic behavior as a function of model complexity, specifically transitioning from overfitting to interpolation and back due to the onset of double descent, marking the point beyond which increasing model capacity allows perfect fitting of training data without necessarily worsening test performance.

12

Publications

747

Citations

21

Authors

16

Institutions

Publications per year

2017–2026

9

Authors

21

  1. FK
    Florent Krząkała

    Centre National de la Recherche Scientifique, France

    3 publications · 164 citations

    3

    h-index

  2. GB
    Giulio Biroli

    Centre National de la Recherche Scientifique, France

    3 publications · 80 citations

    3

    h-index

  3. BL
    Bruno Loureiro

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 111 citations

    2

    h-index

  4. LS
    Levent Sagun

    New York University, United States

    2 publications · 27 citations

    2

    h-index

  5. LZ
    Lenka Zdeborová

    Centre National de la Recherche Scientifique, France

    2 publications · 111 citations

    2

    h-index

1–5 of 21

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 384 citations

    5

    h-index

  2. Journal of Statistical Mechanics Theory and Experiment

    JSTAT · 2 publications · 90 citations

    2

    h-index

  3. Neurocomputing

    1 publications · 22 citations

    1

    h-index

  4. Communications on Pure and Applied Mathematics

    1 publications · 191 citations

    1

    h-index

  5. Quarterly Journal of Experimental Psychology

    1 publications · 12 citations

    1

    h-index

1–5