3 papers
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
Bilateral Distribution Compression: Reducing Both Data Size and Dimensionality
Dominic Broadbent, Nick Whiteley, Robert Allison +1
Existing distribution compression methods reduce the number of observations in a dataset by minimising the Maximum Mean Discrepancy (MMD) between original and compressed sets, but…
stat.ML2026
Conditional Distribution Compression via the Kernel Conditional Mean Embedding
Dominic Broadbent, Nick Whiteley, Robert Allison +1
Existing distribution compression methods, like Kernel Herding (KH), were originally developed for unlabelled data. However, no existing approach directly compresses the conditiona…