Your optimizer, whether Bayesian or not, needs to be able to recognize a low point when it hits one, or else it can't optimize at all! If every point looks the same... (It may learn more about high points, but it must still learn about low points.)
(It may learn more about high points, but it must still learn about low points.)
That's not how Bayesian optimization works. Broadly, the idea is that we use Bayesian optimization when both calculating the value of the target function at a point and calculating its gradient are both expensive or infeasible. Thus, we instead choose points at which to sample the target function, and the samples train a Gaussian process model (or other nonparametric model of functions) that tells us what the function's surface looks like. In such a procedure, we obtain t...
Another month, another rationality quotes thread. The rules are: