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, 6love luzern 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 constr