Will Tebbutt
Title
Cited by
Cited by
Year
The Gaussian Process Autoregressive Regression Model (GPAR)
J Requeima, W Tebbutt, W Bruinsma, RE Turner
22nd International Conference on Artificial Intelligence and Statistics, 2019
212019
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
72020
Sparse Gaussian Process Variational Autoencoders
M Ashman, J So, W Tebbutt, V Fortuin, M Pearce, RE Turner
arXiv preprint arXiv:2010.10177, 2020
52020
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
42020
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
W Tebbutt, A Solin, RE Turner
37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021
32021
Convolutional conditional neural processes for local climate downscaling
A Vaughan, W Tebbutt, JS Hosking, RE Turner
Geoscientific Model Development Discussions, 1-25, 2021
22021
The Gaussian Process Latent Autoregressive Model
R Xia, W Bruinsma, W Tebbutt, RE Turner
Third Symposium on Advances in Approximate Bayesian Inference, 2020
2020
Circular Pseudo-Point Approximations for Scaling Gaussian Processes
W Tebbutt, TD Bui, RE Turner
Advances in Approximate Bayesian Inference, NIPS 2016 Workshop, 2016
2016
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Articles 1–8