Modelling Data with both Sparsity and a Gaussian Random Field: Application to Dark Matter Mass Mapping in Cosmology. - Konstantinos E. Themelis; François Lanusse; Niall Jeffrey; Austin Peel; Jean-Luc Starck; Filipe B. Abdalla (EUSIPCO, 2018)

Fast sampling from Wiener posteriors for image data with dataflow engines. - Niall Jeffrey; Alan Heavens; P. D. Fortio (Astron. Comput., 2018)

Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks. - Niall Jeffrey; Benjamin D. Wandelt (CoRR, 2020)

Probabilistic Mass Mapping with Neural Score Estimation. - Benjamin Remy; François Lanusse; Niall Jeffrey; Jia Liu; Jean-Luc Starck; Ken Osato; Tim Schrabback (CoRR, 2022)

Rediscovering orbital mechanics with machine learning. - Pablo Lemos; Niall Jeffrey; Miles D. Cranmer; Shirley Ho; Peter W. Battaglia (Mach. Learn. Sci. Technol., 2023)

Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison. - Niall Jeffrey; Benjamin D. Wandelt (Mach. Learn. Sci. Technol., 2024)

Transfer learning for multifidelity simulation-based inference in cosmology. - A. A. Saoulis; Davide Piras; Niall Jeffrey; Alessio Spurio Mancini; A. M. G. Ferreira; Benjamin Joachimi (CoRR, 2025)

Multilevel neural simulation-based inference. - Yuga Hikida; Ayush Bharti; Niall Jeffrey; François-Xavier Briol (CoRR, 2025)