  The paper presents a novel approach to direct covariance function learning
for Bayesian optimisation, with particular emphasis on experimental design
problems where an existing corpus of condensed knowledge is present. The method
presented borrows techniques from reproducing kernel Banach space theory
(specifically m-kernels) and leverages them to convert (or re-weight) existing
covariance functions into new, problem-specific covariance functions. The key
advantage of this approach is that rather than relying on the user to manually
select (with some hyperparameter tuning and experimentation) an appropriate
covariance function it constructs the covariance function to specifically match
the problem at hand. The technique is demonstrated on two real-world problems -
specifically alloy design and short-polymer fibre manufacturing - as well as a
selected test function.
