On Second Order Behaviour in Augmented Neural ODEs. - Alexander Norcliffe; Cristian Bodnar; Ben Day; Nikola Simidjievski; Pietro Lió (NeurIPS, 2020)

Neural ODE Processes. - Alexander Norcliffe; Cristian Bodnar; Ben Day; Jacob Moss; Pietro Liò (ICLR, 2021)

Meta-learning using privileged information for dynamics. - Ben Day; Alexander Norcliffe; Jacob Moss; Pietro Liò (CoRR, 2021)

Composite Feature Selection Using Deep Ensembles. - Fergus Imrie; Alexander Norcliffe; Pietro Lió; Mihaela van der Schaar (NeurIPS, 2022)

Learning Feynman Diagrams using Graph Neural Networks. - Harrison Mitchell; Alexander Norcliffe; Pietro Liò (CoRR, 2022)

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. - Alexander Norcliffe; Bogdan Cebere; Fergus Imrie; Pietro Liò; Mihaela van der Schaar (AISTATS, 2023)

Longitudinal Modeling of Multiple Sclerosis using Continuous Time Models. - Alexander Norcliffe; Lev Proleev; Diana Mincu; Fletcher Lee Hartsell; Katherine A. Heller; Subhrajit Roy (CoRR, 2023)

Fourier Neural Differential Equations for learning Quantum Field Theories. - Isaac Brant; Alexander Norcliffe; Pietro Liò (CoRR, 2023)

Faster Training of Neural ODEs Using Gauß-Legendre Quadrature. - Alexander Luke Ian Norcliffe; Marc Peter Deisenroth (Trans. Mach. Learn. Res., 2023)

Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression. - Alexander Luke Ian Norcliffe; Lev Proleev; Diana Mincu; Fletcher Lee Hartsell; Katherine A. Heller; Subhrajit Roy (Trans. Mach. Learn. Res., 2023)

Stochastic Encodings for Active Feature Acquisition. - Alexander Norcliffe; Changhee Lee; Fergus Imrie; Mihaela van der Schaar; Pietro Lio (CoRR, 2025)