Divergence Minimization
Divergence Minimization is a variational inference technique that belongs to the class of optimization approaches used to approximate intractable probability distributions, distinguished by its objective of reducing the discrepancy—measured by a divergence metric such as the Kullback-Leibler divergence—between a tractable variational distribution and the true posterior distribution.
748
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1.6K
Authors
665
Institutions
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2017–2026
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748
Clustering with Bregman Divergences
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon et al. · 2004 · 1.5K citations
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University of California, Berkeley
Berkeley, United States
14 h-index
38 publications
6K citations

Centre National de la Recherche Scientifique
Paris, France
14 h-index
39 publications
5.9K citations
Sony Computer Science Laboratories
Tokyo, Japan
13 h-index
14 publications
900 citations
Universidad Complutense de Madrid
Madrid, Spain
12 h-index
33 publications
650 citations
Columbia University
New York, United States
12 h-index
25 publications
754 citations
University of Florida
Gainesville, United States
11 h-index
25 publications
1.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
10 h-index
23 publications
1.3K citations
Stanford University
Stanford, United States
10 h-index
21 publications
2.6K citations
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Repository · 125 publications · 6.6K citations
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20 publications · 873 citations
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TPAMI · 10 publications · 1K citations
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