Scalable Exact Inference in Multi-Output Gaussian Processes

14 Nov 2019Wessel P. BruinsmaEric PerimWill TebbuttJ. Scott HoskingArno SolinRichard E. Turner

Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling. The key problem with MOGPs is the cubic computational scaling in the number of both inputs (e.g., time points or locations), n, and outputs, p. Current methods reduce this to O(n^3 m^3), where m < p is the desired degrees of freedom... (read more)

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