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William Tebbutt

2 accepted papers

2020

Scalable Exact Inference in Multi-Output Gaussian Processes

ICML 2020poster

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 their computational scaling $O(n^3 p^3)$, which is cubic in the number o…

2019

The Gaussian Process Autoregressive Regression Model (GPAR)

AISTATS 2019poster

Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process…