TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing

Mean-field variational inference (VI) is computationally scalable, but its highlydemanding independence requirement hinders it from being applied to wider scenarios. Although many VI methods that take correlation into account have been proposed, these methods generally are not scalable enough to capture the correlation among data instances, which often arises in applications involving graphs or explicit constraints among instances.