Stopping Bayesian Optimization with Probabilistic Regret Bounds
James T. Wilson
Bayesian optimization is a popular framework for efficiently finding
high-quality solutions to difficult problems based on limited prior
information. As a rule, these algorithms operate by iteratively choosing what
to try next until some predefined budget has been exhausted. We investigate
replacing this de facto stopping rule with an $(\epsilon, \delta)$-criterion:
stop when a solution has been found whose value is within $\epsilon > 0$ of the
optimum with probability at least $1 - \delta$ under the model. Given access to
the prior distribution of problems, we show how to verify this condition in
practice using a limited number of draws from the posterior. For Gaussian
process priors, we prove that Bayesian optimization with the proposed criterion
stops in finite time and returns a point that satisfies the $(\epsilon,
\delta)$-criterion under mild assumptions. These findings are accompanied by
extensive empirical results which demonstrate the strengths and weaknesses of
this approach.