Provably Reliable Large-Scale Sampling from Gaussian Processes. - Anthony Stephenson; Robert Allison; Edward Pyzer-Knapp (CoRR, 2022)

Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression. - Robert Allison; Anthony Stephenson; Samuel F; Edward O. Pyzer-Knapp (NeurIPS, 2023)

Conditional Distribution Compression via the Kernel Conditional Mean Embedding. - Dominic Broadbent; Nick Whiteley; Robert Allison; Tom Lovett (CoRR, 2025)

Vulnerability of Transfer-Learned Neural Networks to Data Reconstruction Attacks in Small-Data Regime. - Tomasz Maciazek; Robert Allison (CoRR, 2025)