| 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 | 64* | 2021 |
| A Kernel Two-Sample Test for Functional Data G Wynne, AB Duncan Journal of Machine Learning Research 23 (73), 1-51, 2022 | 53 | 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 | 43 | 2020 |
| Grassmann Stein variational gradient descent X Liu, H Zhu, JF Ton, G Wynne, A Duncan arXiv preprint arXiv:2202.03297, 2022 | 13 | 2022 |
| A spectral representation of kernel Stein discrepancy with application to goodness-of-fit tests for measures on infinite dimensional Hilbert spaces G Wynne, M Kasprzak, AB Duncan arXiv preprint arXiv:2206.04552, 2022 | 11 | 2022 |
| Statistical depth meets machine learning: Kernel mean embeddings and depth in functional data analysis G Wynne, S Nagy arXiv preprint arXiv:2105.12778, 2021 | 11 | 2021 |
| Variational gaussian processes: A functional analysis view G Wynne, V Wild International Conference on Artificial Intelligence and Statistics, 4955-4971, 2022 | 10* | 2022 |
| Bayes hilbert spaces for posterior approximation G Wynne arXiv preprint arXiv:2304.09053, 2023 | 1 | 2023 |
| Contributions in Functional Data Analysis and Functional-analytic Statistics G Wynne Imperial College London, 2023 | | 2023 |
