MAGMA: inference and prediction using multi-task Gaussian processes with common mean

DOI10.1007/s10994-022-06172-1OpenAlexW4280576960MaRDI QIDQ80870

Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey, Servane Gey, Arthur Leroy, Pierre Latouche

Publication date: May 2022

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

Full work available at URL: https://arxiv.org/abs/2007.10731

zbMATH Keywords

EM algorithmGaussian processesfunctional data analysismulti-task learningcommon mean process

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

MagmaClustR

Uses Software

• L-BFGS-B
• L-BFGS
• WinBUGS
• fda (R)
• GPFDA
• PRMLT

Cites Work

• Smoothing and mean-covariance estimation of functional data with a Bayesian hierarchical model
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Kernels for Vector-Valued Functions: A Review
• Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”
• Gaussian Process Regression Analysis for Functional Data
• The EM Algorithm and Extensions, 2E
• Updating Quasi-Newton Matrices with Limited Storage
• Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian–Wishart processes
• Gaussian Process Functional Regression Modeling for Batch Data
• A Bayesian Model for Sparse Functional Data
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