130 citations · 172 across the 7 of their papers we have counts for
4 papers · 1 filter
Ice Core Dating using Probabilistic Programming
Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite +5
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a functio…
Sparse Gaussian Process Variational Autoencoders
Matthew Ashman, Jonathan So, Will Tebbutt +3
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generat…
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…
The Gaussian Process Autoregressive Regression Model (GPAR)
James Requeima, Will Tebbutt, Wessel Bruinsma +1
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically y…