| Convergence guarantees for Gaussian process means with misspecified likelihoods and smoothness G Wynne, FX Briol, M Girolami Journal of Machine Learning Research 22 (123), 1-40, 2021 | 99 | 2021 |
| A Kernel Two-Sample Test for Functional Data G Wynne, AB Duncan Journal of Machine Learning Research 23 (73), 1-51, 2022 | 81 | 2022 |
| Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions T Karvonen, G Wynne, F Tronarp, C Oates, S Särkkä SIAM/ASA Journal on Uncertainty Quantification 8 (3), 926-958, 2020 | 59 | 2020 |
| Grassmann Stein variational gradient descent X Liu, H Zhu, JF Ton, G Wynne, A Duncan arXiv preprint arXiv:2202.03297, 2022 | 19 | 2022 |
| A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces G Wynne, MJ Kasprzak, AB Duncan Bernoulli 31 (2), 868-893, 2025 | 18 | 2025 |
| Variational gaussian processes: A functional analysis view G Wynne, V Wild International Conference on Artificial Intelligence and Statistics, 4955-4971, 2022 | 18* | 2022 |
| Statistical depth meets machine learning: Kernel mean embeddings and depth in functional data analysis G Wynne, S Nagy International Statistical Review, 2025 | 14 | 2025 |
| Bayes hilbert spaces for posterior approximation G Wynne arXiv preprint arXiv:2304.09053, 2023 | 3 | 2023 |
| On Reconstructing Training Data From Bayesian Posteriors and Trained Models G Wynne arXiv preprint arXiv:2507.18372, 2025 | | 2025 |
| Contributions in Functional Data Analysis and Functional-analytic Statistics G Wynne Imperial College London, 2023 | | 2023 |
