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

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 pro...

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Bibliographic Details
Main Authors: Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Entropy
Subjects:
maximum entropy
log determinant estimation
Bayesian optimisation
Online Access:https://www.mdpi.com/1099-4300/21/6/551

Internet

https://www.mdpi.com/1099-4300/21/6/551

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