Speaker |
James Hensman |
|---|---|
Affiliation |
Lancaster University |
Date |
Friday, 04 March 2016 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts G08 Sir David Davies LT (TBC) |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Gaussian process models are widely used in statistics and machine learning. There are three key challenges to inference that might be tackled using variational methods: inference over the latent function values when the likelihood is non-Gaussian; scaling the computation to large datasets; inference over the kernel-parameters. I’ll show how the variational framework can be used to tackle all of these. In particular, I’ll share recent insights which allow us to interpret the approximation ain an elegant and straightforward way, using variational Bayes over stochastic processes. Finally, I’ll outline how this technology can be used to help tackle contemporary problems in biostatistics. |
Biography |