130 citations · 172 across the 7 of their papers we have counts for
8 papers
Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling
Pedro Sousa, Will Tebbutt, Sadiq Jaffer +3
Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near…
Aardvark weather: end-to-end data-driven weather forecasting
Anna Vaughan, Stratis Markou, Will Tebbutt +8
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning model…
Ice Core Dating using Probabilistic Programming
Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite +5
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a functio…
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Will Tebbutt, Arno Solin, Richard E. Turner
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. Howev…
Sparse Gaussian Process Variational Autoencoders
Matthew Ashman, Jonathan So, Will Tebbutt +3
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generat…
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel P. Bruinsma, Eric Perim, Will Tebbutt +3
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-te…