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
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9.7K
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313
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92
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2017–2026
78
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A unifying view on dataset shift in classification
Jose G. Moreno-Torres, Troy Raeder, Rocío Aláiz-Rodríguez et al. · Pattern Recognition · 2011 · 962 citations
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk et al. · arXiv (Cornell University) · 2019 · 567 citations · Full text
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Google (United States)
Mountain View, United States
11 h-index
52 publications
16.9K citations
University of California, Berkeley
Berkeley, United States
7 h-index
19 publications
11.1K citations
Stanford University
Stanford, United States
6 h-index
24 publications
1.5K citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
8 publications
354 citations

Johns Hopkins University
Baltimore, United States
5 h-index
10 publications
728 citations
Hospital for Sick Children
Toronto, Canada
3 h-index
9 publications
345 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
3 publications
44 citations
SickKids Foundation
Toronto, Canada
3 h-index
9 publications
345 citations
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Repository · 37 publications · 3.2K citations
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Conference · 3 publications · 569 citations
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2 publications · 61 citations
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NEJM · 1 publications · 21 citations
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1 publications · 22 citations
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