collaborators

6 papers

cs.LG2026

Supervised Guidance Training for Infinite-Dimensional Diffusion Models

Elizabeth L. Baker, Alexander Denker, Jes Frellsen

Score-based diffusion models have recently been extended to infinite-dimensional function spaces, with uses such as inverse problems arising from partial differential equations. In…

cs.LG2026

GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains

James Rowbottom, Elizabeth L. Baker, Nick Huang +3

Score-based diffusion models in infinite-dimensional function spaces provide a mathematically principled framework for modelling function-valued data, offering key advantages such…

math.PR2026

Stochastics of shapes and Kunita flows

Stefan Sommer, Gefan Yang, Elizabeth Louise Baker

Stochastic processes of evolving shapes are used in applications including evolutionary biology, where morphology changes stochastically as a function of evolutionary processes. Du…

stat.ML2025

Conditioning Diffusions Using Malliavin Calculus

Jakiw Pidstrigach, Elizabeth Baker, Carles Domingo-Enrich +2

In generative modelling and stochastic optimal control, a central computational task is to modify a reference diffusion process to maximise a given terminal-time reward. Most exist…

cs.LG2025

Infinite-dimensional Diffusion Bridge Simulation via Operator Learning

Gefan Yang, Elizabeth Louise Baker, Michael L. Severinsen +2

The diffusion bridge, which is a diffusion process conditioned on hitting a specific state within a finite period, has found broad applications in various scientific and engineerin…

stat.ML2025

Score matching for bridges without learning time-reversals

Elizabeth L. Baker, Moritz Schauer, Stefan Sommer

We propose a new algorithm for learning bridged diffusion processes using score-matching methods. Our method relies on reversing the dynamics of the forward process and using this…