Bregman Optimization
Bregman Optimization is a variational optimization method that generalizes gradient-based updates by replacing Euclidean distance measures with Bregman divergences to exploit the geometric structure of the underlying parameter space.
195
Publications
15.2K
Citations
452
Authors
239
Institutions
Publications per year
2017–2026
59
Parents
195
Information-theoretic metric learning
Jason V. Davis, Brian Kulis, Prateek Jain et al. · 2007 · 2K citations
Logistic Regression, AdaBoost and Bregman Distances
Michael Collins, Robert E. Schapire, Yoram Singer · Machine Learning · 2002 · 689 citations · Full text
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University of California, Los Angeles
Los Angeles, United States
13 h-index
29 publications
3.7K citations

Harbin Institute of Technology
Harbin, China
9 h-index
29 publications
598 citations
University of Münster
Münster, Germany
5 h-index
9 publications
848 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
5 h-index
8 publications
395 citations

Rice University
Houston, United States
5 h-index
5 publications
487 citations

Chinese Academy of Sciences
Beijing, China
5 h-index
11 publications
146 citations
Georgia Institute of Technology
Atlanta, United States
4 h-index
4 publications
184 citations
École Polytechnique
Palaiseau, France
4 h-index
5 publications
211 citations
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Repository · 13 publications · 410 citations
11
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8 publications · 254 citations
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SIIMS · 7 publications · 3.2K citations
7
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6 publications · 838 citations
6
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5 publications · 317 citations
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