Uncertain Data Modeling
Uncertain Data Modeling is a methodological framework within Gaussian Process Modeling that formally characterizes and propagates input and output uncertainties through probabilistic inference to yield robust predictive distributions.
720
Publications
44.1K
Citations
1.8K
Authors
768
Institutions
Publications per year
2017–2026
243
Parents
720
Dimitris Bertsimas, Melvyn Sim · Operations Research · 2004 · 4.4K citations
Distributionally Robust Convex Optimization
Wolfram Wiesemann, Daniel Kühn, Melvyn Sim · Operations Research · 2014 · 947 citations · Full text
Robust solution to least squares problems with uncertain data
Laurent El Ghaoui, Hervé Lebret · 1997 · 645 citations
1–5 of 720
1.8K
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Hong Kong University of Science and Technology, Hong Kong SAR China
14 publications · 775 citations
12
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11
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9
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9
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1–5 of 1.8K
768

University of Hong Kong
Pok Fu Lam, Hong Kong
19 h-index
50 publications
4.1K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
19 h-index
47 publications
2.7K citations

UNSW Sydney
Sydney, Australia
14 h-index
36 publications
3K citations

Centre National de la Recherche Scientifique
Paris, France
13 h-index
24 publications
702 citations
Purdue University West Lafayette
West Lafayette, United States
12 h-index
38 publications
4K citations
Ludwig-Maximilians-Universität München
Munich, Germany
11 h-index
43 publications
4.4K citations

Northeastern University
Shenyang, China
10 h-index
30 publications
660 citations
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
13 publications
10.6K citations
1–8 of 768
PVLDB · 19 publications · 1.1K citations
17
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TKDE · 18 publications · 1.7K citations
17
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Repository · 22 publications · 777 citations
14
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14 publications · 744 citations
13
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TFS · 10 publications · 581 citations
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