[highd]

Bayesian optimization is often applied on problems where the cost of function evaluation greatly exceeds the cost of any posterior updates etc. In this case sample efficiency is the real benchmark, and you should be looking at realistic problems with d>>2.

[chroem-]

Hi there,

Generally speaking the heuristic for applying BO to optimization problems is that the objective function must take at least as long to evaluate as an iteration of BO. For an equally sample-efficient algorithm with negligible overhead, you can now take twice as many samples and find better optima for a large class of problems.

The shown examples are all 2D because plotting high dimensional space is rather difficult. Also, this is intended as an early proof of concept. A more formal description of the algorithm is coming, along with better examples.