I don't think they directly relate. The premise of the paper seems quite an interesting one and anything that can encodes prior knowledge about the objective fu

I don't think they directly relate. The premise of the paper seems quite an interesting one and anything that can encodes prior knowledge about the objective function while improving the convergence rate is always welcomed, particularly when it is expensive to evaluate. In terms of my original comment I was referring to standard Bayesian Opt (GP) and my experiences with convergence rates when optimising applications. In these algorithms the kernel underlies the accuracy of future predictions and it is important that is reflects the configuration space. For the majority (if not all) applications the parameter space will consist of continuous and categorical parameters. It is vital that the kernel or choices of kernels over various dimensions encodes the structure of these encoded variables.