Online Stochastic Optimization
Online Stochastic Optimization is a stochastic optimization framework in which decisions are made sequentially in real-time under uncertainty, with the distinguishing feature that the underlying probability distributions of random variables are either unknown or revealed incrementally through partial observations, requiring adaptive decision-making at each stage.
244
Publications
105.5K
Citations
606
Authors
259
Institutions
Publications per year
2017–2026
105
Parents
244
Adam: A Method for Stochastic Optimization
Diederik P. Kingma, Jimmy Ba · UvA-DARE (University of Amsterdam) · 2014 · 84.8K citations · Full text
Online Learning for Latent Dirichlet Allocation
Matthew D. Hoffman, Francis Bach, David M. Blei · 2010 · 1.3K citations
Optimal Distributed Online Prediction using Mini-Batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir et al. · arXiv (Cornell University) · 2010 · 563 citations · Full text
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Princeton University
Princeton, United States
12 h-index
19 publications
11.9K citations
Google (United States)
Mountain View, United States
9 h-index
17 publications
9.1K citations

Cornell University
Ithaca, United States
8 h-index
14 publications
513 citations
Massachusetts Institute of Technology
Cambridge, United States
8 h-index
11 publications
445 citations
Tsinghua University
Beijing, China
8 h-index
18 publications
537 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
7 h-index
11 publications
739 citations
University of Illinois Urbana-Champaign
Urbana, United States
7 h-index
11 publications
283 citations
University of Southern California
Los Angeles, United States
7 h-index
12 publications
1.5K citations
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Repository · 43 publications · 1.7K citations
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12 publications · 472 citations
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JMLR · 6 publications · 872 citations
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Conference · 6 publications · 237 citations
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5 publications · 360 citations
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