  Information-theoretic Bayesian optimisation techniques have demonstrated
state-of-the-art performance in tackling important global optimisation
problems. However, current information-theoretic approaches require many
approximations in implementation, introduce often-prohibitive computational
overhead and limit the choice of kernels available to model the objective. We
develop a fast information-theoretic Bayesian Optimisation method, FITBO, that
avoids the need for sampling the global minimiser, thus significantly reducing
computational overhead. Moreover, in comparison with existing approaches, our
method faces fewer constraints on kernel choice and enjoys the merits of
dealing with the output space. We demonstrate empirically that FITBO inherits
the performance associated with information-theoretic Bayesian optimisation,
while being even faster than simpler Bayesian optimisation approaches, such as
Expected Improvement.
