1 citations · 1 across the 6 of their papers we have counts for
6 papers
Scalable Gaussian process inference via neural feature maps
Anthony Stephenson
We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels. We show that the learned feature map can be inter…
The Theory and Practice of Highly Scalable Gaussian Process Regression with Nearest Neighbours
Robert Allison, Tomasz Maciazek, Anthony Stephenson
Gaussian process () regression is a widely used non-parametric modeling tool, but its cubic complexity in the training size limits its use on massive data sets. A practical rem…
Generator-based Graph Generation via Heat Diffusion
Anthony Stephenson, Ian Gallagher, Christopher Nemeth
Graph generative modelling has become an essential task due to the wide range of applications in chemistry, biology, social networks, and knowledge representation. In this work, we…
Further Understanding of a Local Gaussian Process Approximation: Characterising Convergence in the Finite Regime
Anthony Stephenson, Robert Allison, Edward Pyzer-Knapp
We show that common choices of kernel functions for a highly accurate and massively scalable nearest-neighbour based GP regression model (GPnn: \cite{GPnn}) exhibit gradual converg…
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
Robert Allison, Anthony Stephenson, Samuel F +1
The accurate predictions and principled uncertainty measures provided by GP regression incur O(n^3) cost which is prohibitive for modern-day large-scale applications. This has moti…
Provably Reliable Large-Scale Sampling from Gaussian Processes
Anthony Stephenson, Robert Allison, Edward Pyzer-Knapp
When comparing approximate Gaussian process (GP) models, it can be helpful to be able to generate data from any GP. If we are interested in how approximate methods perform at scale…