Online Convex Optimization
Online Convex Optimization is an online learning framework in which a learner sequentially selects points from a fixed convex set and incurs losses defined by a sequence of convex functions, with the objective of minimizing regret relative to the best fixed decision in hindsight.
502
Publications
122.4K
Citations
929
Authors
334
Institutions
Publications per year
2017–2026
266
502
Adam: A Method for Stochastic Optimization
Diederik P. Kingma, Jimmy Ba · UvA-DARE (University of Amsterdam) · 2014 · 84.8K citations · Full text
Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, Satyen Kale · Machine Learning · 2007 · 874 citations · Full text
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Princeton University
Princeton, United States
18 h-index
43 publications
11.7K citations
University of California, Berkeley
Berkeley, United States
17 h-index
47 publications
11.6K citations
Carnegie Mellon University
Pittsburgh, United States
16 h-index
32 publications
3.8K citations
University of Pennsylvania
Philadelphia, United States
16 h-index
32 publications
2K citations
California Institute of Technology
Pasadena, United States
14 h-index
36 publications
1.4K citations
Technion – Israel Institute of Technology
Haifa, Israel
14 h-index
21 publications
1.1K citations

University of Minnesota
Minneapolis, United States
13 h-index
28 publications
1.8K citations
Google (United States)
Mountain View, United States
12 h-index
22 publications
9.9K citations
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Repository · 105 publications · 3.4K citations
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21 publications · 1.2K citations
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9 publications · 982 citations
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Conference · 8 publications · 505 citations
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5 publications · 113 citations
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