  Bayesian optimization (BO) is increasingly employed in critical applications
such as materials design and drug discovery. An increasingly popular strategy
in BO is to forgo the sole reliance on high-fidelity data and instead use an
ensemble of information sources which provide inexpensive low-fidelity data.
The overall premise of this strategy is to reduce the overall sampling costs by
querying inexpensive low-fidelity sources whose data are correlated with
high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO
framework that dramatically outperforms the state-of-the-art technologies in
terms of efficiency, consistency, and robustness. We demonstrate the advantages
of our framework on analytic and engineering problems and argue that these
benefits stem from our two main contributions: (1) we develop a novel
acquisition function for multi-fidelity cost-aware BO that safeguards the
convergence against the biases of low-fidelity data, and (2) we tailor a newly
developed emulator for multi-fidelity BO which enables us to not only
simultaneously learn from an ensemble of multi-fidelity datasets, but also
identify the severely biased low-fidelity sources that should be excluded from
BO.
