417 citations · 1.7k across the 36 of their papers we have counts for
38 papers
Inverse-Free Sparse Variational Gaussian Processes
Stefano Cortinovis, Laurence Aitchison, Stefanos Eleftheriadis +1
Gaussian processes (GPs) offer appealing properties but are costly to train at scale. Sparse variational GP (SVGP) approximations reduce cost yet still rely on Cholesky decompositi…
Sparse Gaussian Processes with Spherical Harmonic Features Revisited
Stefanos Eleftheriadis, Dominic Richards, James Hensman
We revisit the Gaussian process model with spherical harmonic features and study connections between the associated RKHS, its eigenstructure and deep models. Based on this, we intr…
Additive Gaussian Processes Revisited
Xiaoyu Lu, Alexis Boukouvalas, James Hensman
Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power. By using a Gaussian process with additive structure, complex respo…
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Vincent Dutordoir, James Hensman, Mark van der Wilk +3
Neural networks and Gaussian processes are complementary in their strengths and weaknesses. Having a better understanding of their relationship comes with the promise to make each…
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…
Sparse Gaussian Processes with Spherical Harmonic Features
Vincent Dutordoir, Nicolas Durrande, James Hensman
We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations. Our…