  High-dimensional Bayesian optimization (BO) tasks such as molecular design
often require 10,000 function evaluations before obtaining meaningful results.
While methods like sparse variational Gaussian processes (SVGPs) reduce
computational requirements in these settings, the underlying approximations
result in suboptimal data acquisitions that slow the progress of optimization.
In this paper we modify SVGPs to better align with the goals of BO: targeting
informed data acquisition rather than global posterior fidelity. Using the
framework of utility-calibrated variational inference, we unify GP
approximation and data acquisition into a joint optimization problem, thereby
ensuring optimal decisions under a limited computational budget. Our approach
can be used with any decision-theoretic acquisition function and is compatible
with trust region methods like TuRBO. We derive efficient joint objectives for
the expected improvement and knowledge gradient acquisition functions in both
the standard and batch BO settings. Our approach outperforms standard SVGPs on
high-dimensional benchmark tasks in control and molecular design.
