| The Gaussian Process Autoregressive Regression Model (GPAR) J Requeima, W Tebbutt, W Bruinsma, RE Turner 22nd International Conference on Artificial Intelligence and Statistics, 2019 | 48 | 2019 |
| Scalable Exact Inference in Multi-Output Gaussian Processes W Bruinsma, E Perim, W Tebbutt, S Hosking, A Solin, R Turner International Conference on Machine Learning, 1190-1201, 2020 | 36 | 2020 |
| Convolutional conditional neural processes for local climate downscaling A Vaughan, W Tebbutt, JS Hosking, RE Turner Geoscientific Model Development 15 (1), 251-268, 2022 | 30 | 2022 |
| AdvancedHMC. jl: A robust, modular and efficient implementation of advanced HMC algorithms K Xu, H Ge, W Tebbutt, M Tarek, M Trapp, Z Ghahramani Symposium on Advances in Approximate Bayesian Inference, 1-10, 2020 | 25 | 2020 |
| Sparse Gaussian Process Variational Autoencoders M Ashman, J So, W Tebbutt, V Fortuin, M Pearce, RE Turner arXiv preprint arXiv:2010.10177, 2020 | 23 | 2020 |
| Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes W Tebbutt, A Solin, RE Turner Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial …, 2021 | 9 | 2021 |
| Aardvark Weather: end-to-end data-driven weather forecasting A Vaughan, S Markou, W Tebbutt, J Requeima, WP Bruinsma, ... arXiv preprint arXiv:2404.00411, 2024 | 3 | 2024 |
| The Gaussian Process Latent Autoregressive Model R Xia, W Bruinsma, W Tebbutt, RE Turner Third Symposium on Advances in Approximate Bayesian Inference, 2020 | 3 | 2020 |
| Circular Pseudo-Point Approximations for Scaling Gaussian Processes W Tebbutt, TD Bui, RE Turner Advances in Approximate Bayesian Inference, NIPS 2016 Workshop, 2016 | 1 | 2016 |
| Advances in software and spatio-temporal modelling with Gaussian processes W Tebbutt | | 2022 |
