Publication Details

SELECT * FROM publications WHERE Record_Number=11147
Reference TypeConference Proceedings
Author(s)Arenz, O.; Zhong, M.; Neumann, G.
Year2018
TitleEfficient Gradient-Free Variational Inference using Policy Search
Journal/Conference/Book TitleProceedings of the International Conference on Machine Learning
KeywordsVariational Inference, Policy Search, Sampling
AbstractInference from complex distributions is a common problem in machine learning needed for many Bayesian methods. We propose an efficient, gradient-free method for learning general GMM approximations of multi-modal distributions based on recent insights from stochastic search methods. Our method establishes information-geometric trust regions to ensure efficient exploration of the sampling space and stability of the GMM updates, allowing for efficient estimation of multi-variate Gaussian variational distributions. For GMMs, we apply a variational lower bound to decompose the learning objective into sub-problems given by learning the individual mixture components and the coefficients. The number of mixture components is adapted online in order to allow for arbitrary exact approximations. We demonstrate on several domains that we can learn significantly better approximations than competing variational inference methods and that the quality of samples drawn from our approximations is on par with samples created by state-of-the-art MCMC samplers that have been devoted significantly more computational resources.
Link to PDFhttp://www.ausy.tu-darmstadt.de/uploads/Team/OlegArenz/VIPS.pdf

  

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