Model Averaging
Model Averaging is an ensemble method that combines predictions from multiple models by computing a weighted or unweighted average, thereby reducing variance and improving generalization through the aggregation of diverse model outputs.
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2017–2026
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838
Kenneth P. Burnham, David R. Anderson · Sociological Methods & Research · 2004 · 11.6K citations
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage et al. · arXiv (Cornell University) · 2016 · 5.2K citations · Full text
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University of Washington
Seattle, United States
26 h-index
50 publications
18.7K citations

Chinese Academy of Sciences
Beijing, China
23 h-index
79 publications
11.9K citations
Academy of Mathematics and Systems Science
Beijing, China
20 h-index
49 publications
3.2K citations

UNSW Sydney
Sydney, Australia
13 h-index
31 publications
1.8K citations
University of Chinese Academy of Sciences
Beijing, China
12 h-index
16 publications
3.1K citations

International Monetary Fund
Washington D.C., United States
12 h-index
21 publications
1.6K citations
Duke University
Durham, United States
12 h-index
18 publications
1.4K citations

City University of Hong Kong
Hong Kong, Hong Kong
12 h-index
19 publications
1.9K citations
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23 publications · 3.6K citations
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Repository · 20 publications · 6K citations
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