Interaction Regularization

Interaction Regularization is a computational method designed to constrain or penalize interaction effects in predictive models to enhance interpretability, generalizability, and model stability within the broader class of interaction modeling techniques.

18

Publications

996

Citations

73

Authors

37

Institutions

Publications per year

2017–2026

8

Authors

73

  1. XX
    Xing Xie

    Microsoft Research Asia (China), China

    3 publications · 112 citations

    3

    h-index

  2. SM
    Shuangge Ma

    Yale University, United States

    3 publications · 64 citations

    3

    h-index

  3. YG
    Yong Ge

    Rutgers, The State University of New Jersey, United States

    2 publications · 94 citations

    2

    h-index

  4. DL
    Defu Lian

    University of Science and Technology of China, China

    2 publications · 94 citations

    2

    h-index

  5. HH
    Haihui Huang

    Huashan Hospital, China

    1 publications · 38 citations

    1

    h-index

1–5 of 73

Venues

  1. 1

    h-index

  2. Genetic Epidemiology

    1 publications · 16 citations

    1

    h-index

  3. Biostatistics

    1 publications · 383 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5