Stochastic gradient methods have enabled variational inference for high-dimensional models and large data sets. However, the direction of steepest ascent in the parameter space of a statistical model is given not by the commonly used Euclidean gradient, but the natural gradient which premultiplies the Euclidean gradient by the inverse of the Fisher information matrix. Use of natural gradients in optimization can improve convergence significantly, but inverting the Fisher information matrix is daunting in high-dimensions. The contribution of this article is twofold. First, we derive the natural gradient updates of a Gaussian variational approximation in terms of the mean and Cholesky factor of the covariance matrix, and show that these updates depend only on the first derivative of the variational objective function. Second, we derive complete natural gradient updates for structured variational approximations with a minimal conditional exponential family representation, which include highly flexible mixture of exponential family distributions that can fit skewed or multimodal posteriors. These updates, albeit more complex than those presented priorly, account fully for the dependence between the mixing distribution and the distributions of the components. Further experiments will be carried out to evaluate the performance of proposed methods.
View on arXiv