David Duvenaud - A farewell to GPs:  A roadmap for ultra-scalable simulation and inference in SPDEs
Channel: Secondmind
Date: 2023-01-17

This talk sketches a plan for constructing highly-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 could be learned automatically by adding auxiliary variables to an approximate variational posterior.  We'll also demonstrate a new contribution to parallelizing adaptive SPDE solvers, allowing stateless sampling of entire Brownian sheets of any dimension.

References:
- Scalable Gradients for Stochastic Differential Equations
https://arxiv.org/abs/2001.01328
- A Complete Recipe for Stochastic Gradient MCMC
https://papers.nips.cc/paper/2015/file/9a4400501febb2a95e79248486a5f6d3-Paper.pdf


Speaker: 
David Duvenaud (University of Toronto), Google scholar profile can be found at https://scholar.google.com/citations?user=ZLpO3XQAAAAJ.

This talk was given at Secondmind Labs, as a part of our (virtual) research seminar. Our research seminar is where we exchange ideas with guest speakers, keeping you up to date with the latest developments and inspiring research topics. Occasionally, Secondmind researchers present their own work as well. You can find a complete list of speakers at https://www.secondmind.ai/labs/seminars/. Learn more about Secondmind Labs at https://www.secondmind.ai/labs/​