  The ultimate goal of optimization is to find the minimizer of a target
function.However, typical criteria for active optimization often ignore the
uncertainty about the minimizer. We propose a novel criterion for global
optimization and an associated sequential active learning strategy using
Gaussian processes.Our criterion is the reduction of uncertainty in the
posterior distribution of the function minimizer. It can also flexibly
incorporate multiple global minimizers. We implement a tractable approximation
of the criterion and demonstrate that it obtains the global minimizer
accurately compared to conventional Bayesian optimization criteria.
