65 citations · 76 across the 6 of their papers we have counts for
5 papers · 1 filter
GPflux: A Library for Deep Gaussian Processes
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro +7
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the v…
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…
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
Andrés F. López-Lopera, ST John, Nicolas Durrande
Gaussian process (GP) modulated Cox processes are widely used to model point patterns. Existing approaches require a mapping (link function) between the unconstrained GP and the po…
Large-Scale Cox Process Inference using Variational Fourier Features
S. T. John, James Hensman
Gaussian process modulated Poisson processes provide a flexible framework for modelling spatiotemporal point patterns. So far this had been restricted to one dimension, binning to…