no code implementations • 25 Dec 2023 • Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R. Andersson, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, Matthew Willson
Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather, to planning renewable energy use.
4 code implementations • 24 Dec 2022 • Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, Peter Battaglia
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
1 code implementation • 2 Apr 2021 • Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, Rachel Prudden, Amol Mandhane, Aidan Clark, Andrew Brock, Karen Simonyan, Raia Hadsell, Niall Robinson, Ellen Clancy, Alberto Arribas, Shakir Mohamed
To address these challenges, we present a Deep Generative Model for the probabilistic nowcasting of precipitation from radar.
no code implementations • NeurIPS 2018 • Alexandre Marques, Remi Lam, Karen Willcox
We introduce an algorithm to locate contours of functions that are expensive to evaluate.
no code implementations • NeurIPS 2017 • Remi Lam, Karen Willcox
We consider the task of optimizing an objective function subject to inequality constraints when both the objective and the constraints are expensive to evaluate.
no code implementations • NeurIPS 2016 • Remi Lam, Karen Willcox, David H. Wolpert
We consider the problem of optimizing an expensive objective function when a finite budget of total evaluations is prescribed.