Adaptive Sampling
Adaptive Sampling is a spatial sampling strategy that iteratively selects sample locations based on previously observed data, distinguishing itself from non-adaptive methods by dynamically adjusting the sampling design to optimize information gain or estimation accuracy.
1.3K
Publications
73K
Citations
3.6K
Authors
1.2K
Institutions
Publications per year
2017–2026
488
1.3K
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia et al. · 2008 · 4.4K citations
Jasper A. Vrugt, Cajo J. F. ter Braak, Cees Diks et al. · International Journal of Nonlinear Sciences and Numerical Simulation · 2009 · 1.2K citations
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Stanford University
Stanford, United States
21 h-index
44 publications
3.9K citations

Centre National de la Recherche Scientifique
Paris, France
18 h-index
81 publications
3.9K citations
University of California, Berkeley
Berkeley, United States
16 h-index
48 publications
3.7K citations
Massachusetts Institute of Technology
Cambridge, United States
15 h-index
50 publications
3.4K citations
University of Southern California
Los Angeles, United States
15 h-index
42 publications
1.9K citations
University of California San Diego
San Diego, United States
14 h-index
32 publications
1.4K citations
Georgia Institute of Technology
Atlanta, United States
14 h-index
28 publications
2.4K citations

Pennsylvania State University
State College, United States
14 h-index
24 publications
5.1K citations
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TOG · 25 publications · 2.2K citations
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Repository · 40 publications · 976 citations
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23 publications · 945 citations
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15 publications · 480 citations
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JCTC · 12 publications · 1.5K citations
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