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Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach.
Cubic convolution interpolation for digital image processing
Robert Keys · 1981
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Circulant preconditioners for toeplitz-block matrices
Tony Chan and Julia Olkin · 1994
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Gaussian processes for regression
Christopher Williams and Carl Rasmussen · 1996
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Efficient implementation of Gaussian processes, 1996
Mark Gibbs and David MacKay · 1996
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A unifying view of sparse approximate Gaussian process regression
Joaquin Quiñonero-Candela and Carl Rasmussen · 2005
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Semiparametric latent factor models
Matthias Seeger, Yee-Whye Teh, and Michael Jordan · 2005
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Fast large scale Gaussian process regression using approximate matrix-vector products
Vikas Raykar and Ramani Duraiswami · 2007
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Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes
Michael Osborne, Stephen Roberts, Alex Rogers, Sarvapali Ramchurn, and Nicholas Jennings · 2008
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Fast Gaussian process methods for point process intensity estimation
John Cunningham, Krishna Shenoy, and Maneesh Sahani · 2008
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Efficient multioutput Gaussian processes through variational inducing kernels
Mauricio Álvarez, David Luengo, Michalis Titsias, and Neil D Lawrence · 2010
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Kernels for vector-valued functions: A review
Mauricio Álvarez, Lorenzo Rosasco, Neil Lawrence, et al · 2012
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Adadelta: an adaptive learning rate method
Matthew Zeiler · 2012
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Collaborative multi-output Gaussian processes
Trung Nguyen, Edwin Bonilla, et al · 2014
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Fast kernel learning for multidimensional pattern extrapolation
Andrew Wilson, Elad Gilboa, John Cunningham, and Arye Nehorai · 2014
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Thoughts on massively scalable Gaussian processes
Andrew Wilson, Christoph Dann, and Hannes Nickisch · 2015
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Scaling multidimensional inference for structured Gaussian processes
Elad Gilboa, Yunus Saatçi, and John Cunningham · 2015
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Kernel interpolation for scalable structured Gaussian processes (kiss-gp)
Andrew Wilson and Hannes Nickisch · 2015
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Large-scale log-determinant computation through stochastic Chebyshev expansions
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David Fong and Michael Saunders · 2012
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GPy: A gaussian process framework in python
GPy · 2012
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Insu Han, Dmitry Malioutov, and Jinwoo Shin · 2015
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Preconditioning kernel matrices
Kurt Cutajar, Michael Osborne, John Cunningham, and Maurizio Filippone · 2016
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