Martin Jørgensen - Bézier Gaussian Processes
Channel: Secondmind
Date: 2022-12-23

Abstract:
I will introduce Bézier Gaussian Processes which is a variational class of polynomial GPs. With their polynomial bases being the Bernstein polynomials, we can make them have similar behaviour to some of the most common stationary kernels. Most important I will introduce what we call the Bézier buttress, which makes the framework scale to both high number of observations and a high number of input features. The scaling in observations stems from the not having to compute matrix inverses and determinants. The scaling in features stems from only a small increase in compute by adding more "summarising points", in this framework named control points. Lastly, I will go into some future directions for Bézier GPs.

References:
- Bézier Gaussian Processes for Tall and Wide Data, In Advances in Neural Information Processing Systems (NeurIPS), 2022: https://arxiv.org/pdf/2209.00343.pdf

Speaker: 
Martin Jørgensen (University of Oxford), personal website can be found at https://jorgensenmart.github.io/.

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/​