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stat.ML2019
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models
Pavel Berkovich, Eric Perim, Wessel Bruinsma
A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved la…
stat.ML2019
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel P. Bruinsma, Eric Perim, Will Tebbutt +3
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-te…