  Bayesian quadrature (BQ) is a model-based numerical integration method that
is able to increase sample efficiency by encoding and leveraging known
structure of the integration task at hand. In this paper, we explore priors
that encode invariance of the integrand under a set of bijective
transformations in the input domain, in particular some unitary
transformations, such as rotations, axis-flips, or point symmetries. We show
initial results on superior performance in comparison to standard Bayesian
quadrature on several synthetic and one real world application.
