Bayesian optimisation (BO) is an optimisation method which incrementally builds a statistical model of the objective function to refine its search. Unfortunately, due to the curse of dimensionality, BO can fail to converge in problems with many dimensions. In this talk, I will show how better priors for BO can result in orders of magnitude improvements in convergence for problems with a known structure. I will introduce a class of models that are both useful and support inference at a reasonable computational cost.
See more on this video at www.microsoft.com/en-us/research/video/bayesian-optimisation-many-dimensions-bespoke-models/