MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning

Blog Article

Efficient CACAY OIL 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 Strap-ons 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.