Multi-Task Bayesian Learning is a multi-task learning framework that employs Bayesian inference to jointly model task-related uncertainties and dependencies, thereby enabling information sharing across tasks through a common prior distribution.
59
Publications
3.6K
Citations
207
Authors
96
Institutions
Publications per year
2017–2026
35
Variants
Mtbl
Parents
Publications
59
Learning Gaussian processes from multiple tasks
Kai Yu, Volker Tresp, Anton Schwaighofer · 2005 · 391 citations
A Tutorial on Bayesian Optimization
Peter I. Frazier · arXiv (Cornell University) · 2018 · 376 citations · Full text
Hierarchical Bayesian domain adaptation
Jenny Rose Finkel, Christopher D. Manning · 2009 · 175 citations · Full text
1–5 of 59
Authors
207
- DB
3
h-index
- LC
3
h-index
- AF
2
h-index
- VT
2
h-index
- KY
2
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1–5 of 207
Institutions
96
Duke University
Durham, United States
3 h-index
9 publications
276 citations
Harbin Institute of Technology
Harbin, China
2 h-index
5 publications
90 citations
Radboud University Nijmegen
Nijmegen, The Netherlands
2 h-index
2 publications
66 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
6 publications
147 citations
Tsinghua University
Beijing, China
2 h-index
8 publications
135 citations
Ministry of Industry and Information Technology
Beijing, China
2 h-index
5 publications
90 citations
Siemens (Germany)
Munich, Germany
2 h-index
3 publications
862 citations
Harvard University
Cambridge, United States
2 h-index
3 publications
225 citations
1–8 of 96
Venues
- arXiv (Cornell University)
Repository · 9 publications · 738 citations
9
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- Neural Information Processing Systems
Conference · 2 publications · 160 citations
2
h-index
- Smart Materials and Structures
SMS · 2 publications · 37 citations
2
h-index
- IEEE Transactions on Knowledge and Data Engineering
TKDE · 2 publications · 104 citations
2
h-index
- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 2 publications · 42 citations
2
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1–5