Ensemble Stability
Ensemble Stability is a methodological framework in machine learning that quantifies the robustness of ensemble models by measuring the consistency of their predictive outputs under perturbations or variations in training data, model initialization, or submodel composition.
43
Publications
2.1K
Citations
130
Authors
87
Institutions
Publications per year
2017–2026
15
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1–5 of 43
130
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NOAA National Centers for Environmental Prediction, United States
2 publications · 45 citations
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2
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1–5 of 130
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University of Siena
Siena, Italy
2 h-index
2 publications
144 citations

Tel Aviv University
Tel Aviv, Israel
2 h-index
5 publications
85 citations
University of Technology Sydney
Sydney, Australia
2 h-index
6 publications
219 citations
NOAA National Centers for Environmental Prediction
College Park, United States
2 h-index
2 publications
45 citations
General Electric (United States)
Boston, United States
2 h-index
2 publications
71 citations

INSEAD
Fontainebleau, France
2 h-index
2 publications
144 citations
National Oceanic and Atmospheric Administration
Washington, United States
2 h-index
2 publications
45 citations
Danish Meteorological Institute
Copenhagen, Denmark
1 h-index
1 publications
49 citations
1–8 of 87
Repository · 2 publications · 25 citations
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2 publications · 55 citations
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Conference · 2 publications · 79 citations
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1 publications · 26 citations
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1 publications · 35 citations
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