  Efficient approximation lies at the heart of large-scale machine learning
problems. In this paper, we propose a novel, robust maximum entropy algorithm,
which is capable of dealing with hundreds of moments and allows for
computationally efficient approximations. We showcase the usefulness of the
proposed method, its equivalence to constrained Bayesian variational inference
and demonstrate its superiority over existing approaches in two applications,
namely, fast log determinant estimation and information-theoretic Bayesian
optimisation.
