Multi-task Gaussian Processes
Multi-Task Gaussian Processes is a probabilistic model that extends the standard Gaussian Process framework by enabling the joint modeling of multiple related tasks through shared latent functions or covariance structures, thereby capturing inter-task dependencies while maintaining task-specific characteristics.
62
Publications
4.6K
Citations
199
Authors
83
Institutions
Publications per year
2017–2026
31
Parents
62
Learning Gaussian processes from multiple tasks
Kai Yu, Volker Tresp, Anton Schwaighofer · 2005 · 391 citations
Learning to learn with the informative vector machine
Neil D. Lawrence, John Platt · 2004 · 334 citations
Multi-task warped Gaussian process for personalized age estimation
Yu Zhang, Dit‐Yan Yeung · 2010 · 195 citations
Computing, Computer Science, Aging +15
1–5 of 62
199
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University of California, Los Angeles, United States
3 publications · 181 citations
3
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3
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1–5 of 199
83

University of Edinburgh
Edinburgh, United Kingdom
5 h-index
11 publications
3.1K citations

The University of Sydney
Sydney, Australia
4 h-index
13 publications
414 citations
University of Oxford
Oxford, United Kingdom
4 h-index
9 publications
922 citations
Tsinghua University
Beijing, China
3 h-index
5 publications
102 citations
University of Sheffield
Sheffield, United Kingdom
3 h-index
6 publications
654 citations
Australian Centre for Robotic Vision
Brisbane, Australia
3 h-index
11 publications
288 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
8 publications
472 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
5 publications
193 citations
1–8 of 83
Repository · 6 publications · 305 citations
6
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Conference · 3 publications · 199 citations
3
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Repository · 1 publications · 55 citations
1
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Repository · 1 publications · 14 citations
1
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Repository · 1 publications · 21 citations
1
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1–5