  Sparse Gaussian Processes are a key component of high-throughput Bayesian
Optimisation (BO) loops; however, we show that existing methods for allocating
their inducing points severely hamper optimisation performance. By exploiting
the quality-diversity decomposition of Determinantal Point Processes, we
propose the first inducing point allocation strategy designed specifically for
use in BO. Unlike existing methods which seek only to reduce global uncertainty
in the objective function, our approach provides the local high-fidelity
modelling of promising regions required for precise optimisation. More
generally, we demonstrate that our proposed framework provides a flexible way
to allocate modelling capacity in sparse models and so is suitable broad range
of downstream sequential decision making tasks.
