High-dimensional Overfitting

High-Dimensional Overfitting is a type of model overfitting that occurs when a learning algorithm captures noise or spurious correlations in high-dimensional feature spaces, leading to poor generalization despite good fit on training data.

66

Publications

7.5K

Citations

218

Authors

113

Institutions

Publications per year

2017–2026

46

Authors

218

  1. CH
    Christian Hansen

    University of Chicago, United States

    2 publications · 3.2K citations

    2

    h-index

  2. PP
    Partha P. Mitra

    Cold Spring Harbor Laboratory, United States

    2 publications · 172 citations

    2

    h-index

  3. MN
    Masaya Nakata

    Yokohama National University, Japan

    1 publications · 127 citations

    2

    h-index

  4. BX
    Bing Xue

    Victoria University of Wellington, New Zealand

    2 publications · 249 citations

    2

    h-index

  5. KM
    Kezhi Mao

    Nanyang Technological University, Singapore

    2 publications · 32 citations

    2

    h-index

1–5 of 218

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 274 citations

    6

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 35 citations

    2

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 175 citations

    2

    h-index

  4. 2

    h-index

  5. Journal of Computational Biology

    1 publications · 23 citations

    1

    h-index

1–5