4 papers
Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding
Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy
Standard Adjacency Spectral Embedding (ASE) relies on a global low-rank assumption often incompatible with the sparse, transitive structure of real-world networks, causing local ge…
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…
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…
Generalisation and benign over-fitting for linear regression onto random functional covariates
Andrew Jones, Nick Whiteley
We study theoretical predictive performance of ridge and ridge-less least-squares regression when covariate vectors arise from evaluating random, means-square continuous functi…