Natural Gradient Variational Inference

Natural gradient variational inference (NGVI) is a technique for approximating complex probability distributions, primarily focusing on efficiently optimizing variational parameters to improve the accuracy of posterior estimations. Current research emphasizes developing and analyzing stochastic NGVI algorithms, particularly within the context of Gaussian mixture models and their application to Bayesian neural networks and differentiable architecture search. This approach offers improved efficiency and accuracy compared to standard gradient methods, impacting fields like Bayesian deep learning and automated machine learning by enabling more scalable and effective probabilistic modeling.