3 papers
stat.ML2026
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…
stat.ML2026
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…
stat.ML2026
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…