Abstract: State space approximations and pseudo point approximations can be combined in a principled manner to yield scalable approximate inference algorithms for sums of separable Gaussian processes. In this talk, I will: 1. show how this combination can be performed for variational pseudo point approximations via a simple conditional independence result, 2. discuss how existing exact inference algorithms for state space models can be re-purposed for approximate inference, 3. interpret existing related work in light of our work, and 4. briefly discuss some experimental results in a spatio-temporal context. For more info, please see our recent UAI paper.
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Will Tebbutt (University of Cambridge)
About the presenter: Will is a PhD student with Rich Turner in the Machine Learning Group at Cambridge, and is interested in probabilistic modelling in general. He is particularly interested in Gaussian processes: how to specify and scale them in large spatio-temporal settings, how best to write software to work with them, and challenges faced in climate science for which they might be helpful.