Multi-Information Source Optimization

Jialei Wang, Matthias Poloczek, Peter I. Frazier

Abstract:

We consider Bayesian methods for multi-information source optimization (MISO),
in which we seek to optimize an expensive-to-evaluate black-box objective function
while also accessing cheaper but biased and noisy approximations (“information
sources”). We present a novel algorithm that outperforms the state of the art for this
problem by using a Gaussian process covariance kernel better suited to MISO than
those used by previous approaches, and an acquisition function based on a one-step
optimality analysis supported by efficient parallelization. We also provide a novel
technique to guarantee the asymptotic quality of the solution provided by this
algorithm. Experimental evaluations demonstrate that this algorithm consistently
finds designs of higher value at less cost than previous approaches.