Hidden Variable Selection

Hidden Variable Selection is a methodological framework in statistical modeling and machine learning that identifies latent or unobserved variables influencing observed data, distinguishing itself by systematically selecting such hidden factors to improve model interpretability and predictive accuracy.

7

Publications

547

Citations

19

Authors

14

Institutions

Publications per year

2017–2026

2

Authors

19

  1. BL
    Bo Li

    Chinese Academy of Sciences, China

    1 publications · 14 citations

    1

    h-index

  2. SR
    Sunand Raghupathi

    Columbia University, United States

    1 publications · 115 citations

    1

    h-index

  3. HL
    Hod Lipson

    Cornell University, United States

    1 publications · 115 citations

    1

    h-index

  4. CC
    Cheng Chen

    University of Science and Technology Beijing, China

    1 publications · 14 citations

    1

    h-index

  5. EP
    Eero P. Simoncelli

    New York University, United States

    1 publications · 51 citations

    1

    h-index

1–5 of 19

Venues

  1. Neurocomputing

    2 publications · 170 citations

    2

    h-index

  2. Neural Processing Letters

    1 publications · 14 citations

    1

    h-index

  3. Nature Computational Science

    1 publications · 115 citations

    1

    h-index

  4. 1

    h-index

  5. IEEE Transactions on Image Processing

    1 publications · 51 citations

    1

    h-index

1–5