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Scalable simulation and inference in non-Gaussian stochastic PDEs

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If you have a question about this talk, please contact James Allingham .

This talk presents early results along a path to scalable approximate inference schemes in large spatiotemporal models, such as weather or molecular dynamics simulations. Specifically, we’ll show how existing heuristics for scaling physical models such as coarse grids or mutli-scale temporal models can be learned automatically as auxiliary variables in variational posteriors. We’ll also demonstrate a new contribution to parallelizing adaptive SPDE solvers, allowing stateless sampling of entire Brownian sheets of any dimension. Finally, we’ll show how to extend stochastic variational inference in SDEs to include arbitrary jump processes.

This talk is part of the Machine Learning Reading Group @ CUED series.

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