[morelandjs]

Thanks, that's useful. My impression of Bayesian parameter optimization using Gaussian processes is that it is quite good when the data has a more or less constant correlation length across the evaluation points as in your example.

When there are large correlation lengths in some regions of the dataset and small correlation lengths elsewhere, it often over (or under) shoots the curvature of the hypersurface.