Stochastic variational hierarchical mixture of sparse Gaussian processes for regression

DOI10.1007/S10994-018-5721-5zbMath1480.62059OpenAlexW2879687780WikidataQ118322654 ScholiaQ118322654MaRDI QIDQ1722733

Son Lam Phung, Abdesselam Bouzerdoum, Thi Nhat Anh Nguyen

Publication date: 18 February 2019

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-018-5721-5

zbMATH Keywords

Gaussian processeshierarchical structuregraphical modelvariational inference

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

JGPR: a computationally efficient multi-target Gaussian process regression algorithm ⋮ Large scale multi-output multi-class classification using Gaussian processes

Uses Software

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• L-BFGS
• LBFGS-B
• GPy
• GitHub
• ADADELTA
• PRMLT

Cites Work

• Sparse On-Line Gaussian Processes
• PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
• Algorithm 778: L-BFGS-B
• Bayesian regression and classification using mixtures of Gaussian processes
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