J. Scott Hosking

According to our database, J. Scott Hosking authored at least 13 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

FastNet: Improving the physical consistency of machine-learning weather prediction models through loss function design.

Tom Dunstan, Oliver Strickson, Thusal Bennett, Jack Bowyer, Matthew Burnand, James Chappell, Alejandro Coca-Castro, Kirstine Ida Dale, Eric G. Daub, Noushin Eftekhari, Manvendra Janmaijaya, Jon Lillis, David Salvador Jasin, Nathan Simpson, Ryan Sze-Yin Chan, Mohamad Elmasri, Lydia Allegranza France, Sam Madge, Levan Bokeria, Hannah Brown, Tom Dodds, Anna-Louise Ellis, David Llewellyn-Jones, Theo McCaie, Sophia Moreton, Tom Potter, James Robinson, Adam A. Scaife, Iain Stenson, David Walters, Karina Bett-Williams, Louisa van Zeeland, Peter Yatsyshin, J. Scott Hosking

CoRR, September, 2025

End-to-end data-driven weather prediction.

Anna Allen, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, Matthew Chantry, J. Scott Hosking, Richard E. Turner

Nat., 2025

2024

Defining error accumulation in ML atmospheric simulators.

Raghul Parthipan, Mohit Anand, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik

CoRR, 2024

Aardvark Weather: end-to-end data-driven weather forecasting.

Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, J. Scott Hosking, Richard E. Turner

CoRR, 2024

Sea ice detection using concurrent multispectral and synthetic aperture radar imagery.

Martin S. J. Rogers, Maria Fox, Andrew Fleming, Louisa van Zeeland, Jeremy Wilkinson, J. Scott Hosking

CoRR, 2024

2023

Autoregressive Conditional Neural Processes.

Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement.

Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew Lazzara, Daniel C. Jones, J. Scott Hosking, Richard E. Turner

CoRR, 2022

Ice Core Dating using Probabilistic Programming.

Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser

CoRR, 2022

Kernel Learning for Explainable Climate Science.

Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, J. Scott Hosking

CoRR, 2022

Stochastic Parameterizations: Better Modelling of Temporal Correlations using Probabilistic Machine Learning.

Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik

CoRR, 2022

2021

Convolutional conditional neural processes for local climate downscaling.

Anna Vaughan, William Tebbutt, J. Scott Hosking, Richard E. Turner

CoRR, 2021

2020

Ensembling geophysical models with Bayesian Neural Networks.

Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Scalable Exact Inference in Multi-Output Gaussian Processes.

Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner

Proceedings of the 37th International Conference on Machine Learning, 2020