55 citations · 97 across the 8 of their papers we have counts for
3 papers · 1 filter
Ensembling geophysical models with Bayesian Neural Networks
Ushnish Sengupta, Matt Amos, J. Scott Hosking +3
Ensembles of geophysical models improve projection accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bay…
Convergence of Sparse Variational Inference in Gaussian Processes Regression
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling. However, their use is often impeded for data with…
Variational Orthogonal Features
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Sparse stochastic variational inference allows Gaussian process models to be applied to large datasets. The per iteration computational cost of inference with this method is $\math…