Multi-output Gaussian Processes
Multi-Output Gaussian Processes is a Gaussian process model that extends the standard Gaussian process framework to jointly model multiple correlated output variables by defining a structured covariance function across both input domains and output dimensions.
73
Publications
3.6K
Citations
213
Authors
103
Institutions
Publications per year
2017–2026
55
Variants
Multi-output Gaussian Process
73
Sparse Convolved Gaussian Processes for Multi-output Regression
Mauricio A. Álvarez, Neil D. Lawrence · 2008 · 176 citations
Computing, Data Science, Forecasting +13
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University of Sheffield
Sheffield, United Kingdom
6 h-index
7 publications
471 citations
University of Oxford
Oxford, United Kingdom
3 h-index
8 publications
800 citations
University of Southampton
Southampton, United Kingdom
3 h-index
8 publications
662 citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
12 publications
496 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
9 publications
135 citations
University of Michigan–Dearborn
Dearborn, United States
3 h-index
3 publications
102 citations
Duke University
Durham, United States
2 h-index
6 publications
86 citations

Chongqing University
Chongqing, China
2 h-index
4 publications
120 citations
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Repository · 10 publications · 291 citations
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4 publications · 394 citations
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3 publications · 376 citations
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2 publications · 98 citations
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Conference · 2 publications · 200 citations
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