Dataset Shift Robustness

Dataset Shift Robustness is a property of a machine learning model that enables it to maintain consistent performance across different data distributions by inherently adapting to shifts between training and test datasets without requiring retraining or recalibration.

88

Publications

9.7K

Citations

313

Authors

92

Institutions

Publications per year

2017–2026

78

Authors

313

  1. BL
    Balaji Lakshminarayanan

    Google (United States), United States

    5 publications · 1.9K citations

    5

    h-index

  2. SS
    Suchi Saria

    Johns Hopkins University, United States

    5 publications · 369 citations

    5

    h-index

  3. AS
    Adarsh Subbaswamy

    Johns Hopkins University, United States

    5 publications · 369 citations

    5

    h-index

  4. NM
    Norman Mu

    Google (United States), United States

    4 publications · 1.8K citations

    4

    h-index

  5. DH
    Dan Hendrycks

    University of California, Berkeley, United States

    4 publications · 1.8K citations

    4

    h-index

1–5 of 313

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 3.2K citations

    21

    h-index

  2. 3

    h-index

  3. 2

    h-index

  4. New England Journal of Medicine

    NEJM · 1 publications · 21 citations

    1

    h-index

  5. Journal of Biomedical Informatics

    1 publications · 22 citations

    1

    h-index

1–5