A theory of representation learning gives a deep generalisation of kernel methods. - Adam X. Yang; Maxime Robeyns; Edward Milsom; Ben Anson; Nandi Schoots; Laurence Aitchison (ICML, 2023)

An improved variational approximate posterior for the deep Wishart process. - Sebastian W. Ober; Ben Anson; Edward Milsom; Laurence Aitchison (UAI, 2023)

Convolutional Deep Kernel Machines. - Edward Milsom; Ben Anson; Laurence Aitchison (ICLR, 2024)

Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines. - Edward Milsom; Ben Anson; Laurence Aitchison (NeurIPS, 2024)

Flexible infinite-width graph convolutional networks and the importance of representation learning. - Ben Anson; Edward Milsom; Laurence Aitchison (CoRR, 2024)

Flexible Infinite-Width Graph Convolutional Neural Networks. - Ben Anson; Edward Milsom; Laurence Aitchison (Trans. Mach. Learn. Res., 2025)

Function-Space Learning Rates. - Edward Milsom; Ben Anson; Laurence Aitchison (CoRR, 2025)