<p>As a general rule of thumb, Bayesian optimisation becomes ineffective when the dimension of the search space is > 15. This will obviously depend on volume and utility function landscape. Check <a href="https://arxiv.org/abs/1902.10675" rel="nofollow noreferrer">https://arxiv.org/abs/1902.10675</a> for an example of bayesian optimisation coupled with some reversible dimensional reduction (an auto-encoder there).</p>
Max dimension for Bayesian Optimization (GPyOpt, GPFlow)
<p>Does anybody know how quickly the Bayesian Optimization algorithm slows down as a function of the dimension of the search space?  What is a good estimate of the maximum dimension that one can reasonably use?  I am thinking especially of GPyOpt and GPFlowOpt.</p>