65 citations · 220 across the 24 of their papers we have counts for
3 papers · 1 filter
A Tutorial on Sparse Gaussian Processes and Variational Inference
Felix Leibfried, Vincent Dutordoir, ST John +1
Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems. For example, if we consider regre…
Amortized variance reduction for doubly stochastic objectives
Ayman Boustati, Sattar Vakili, James Hensman +1
Approximate inference in complex probabilistic models such as deep Gaussian processes requires the optimisation of doubly stochastic objective functions. These objectives incorpora…
A Framework for Interdomain and Multioutput Gaussian Processes
Mark van der Wilk, Vincent Dutordoir, ST John +3
One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for…