ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models

• URL: http://arxiv.org/abs/2106.12248v1
• Date: Wed, 23 Jun 2021 09:09:01 GMT
• Title: ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
• Authors: Louis Rouillard (PARIETAL, Inria, CEA), Demian Wassermann (PARIETAL, Inria, CEA)
• Abstract summary: We develop a novel methodology that automatically produces a variatonal family dual to a target parameter. We demonstrate the capability of our method on simulated data, as well as a challenging high-dimensional brain parcellation experiment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frequently, population studies feature pyramidally-organized data represented using Hierarchical Bayesian Models (HBM) enriched with plates. These models can become prohibitively large in settings such as neuroimaging, where a sample is composed of a functional MRI signal measured on 64 thousand brain locations, across 4 measurement sessions, and at least tens of subjects. Even a reduced example on a specific cortical region of 300 brain locations features around 1 million parameters, hampering the usage of modern density estimation techniques such as Simulation-Based Inference (SBI). To infer parameter posterior distributions in this challenging class of problems, we designed a novel methodology that automatically produces a variational family dual to a target HBM. This variatonal family, represented as a neural network, consists in the combination of an attention-based hierarchical encoder feeding summary statistics to a set of normalizing flows. Our automatically-derived neural network exploits exchangeability in the plate-enriched HBM and factorizes its parameter space. The resulting architecture reduces by orders of magnitude its parameterization with respect to that of a typical SBI representation, while maintaining expressivity. Our method performs inference on the specified HBM in an amortized setup: once trained, it can readily be applied to a new data sample to compute the parameters' full posterior. We demonstrate the capability of our method on simulated data, as well as a challenging high-dimensional brain parcellation experiment. We also open up several questions that lie at the intersection between SBI techniques and structured Variational Inference.

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