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

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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 click here with hundreds of moments and allows for computationally efficient approximations.We showcase the usefulness of the proposed method, superdry baseball top 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.