Bayesian inference via sparse Hamiltonian flows
Main Conference Track
Authors: Naitong Chen, Zuheng Xu, Trevor Campbell

A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost.  Although past work has shown empirically that there often exists a coreset with low inferential error, efficiently constructing such a coreset remains a challenge.  Current methods tend to be slow, require a secondary inference step after coreset construction, and do not provide bounds on the data marginal evidence.  In this work, we introduce a new method---sparse Hamiltonian flows---that addresses all three of these challenges.  The method involves first subsampling the data uniformly, and then optimizing a Hamiltonian flow parametrized by coreset weights and including periodic momentum quasi-refreshment steps.  Theoretical results show that the method enables an exponential compression of the dataset in a representative model, and that the quasi-refreshment steps reduce the KL divergence to the target.  Real and synthetic experiments demonstrate that sparse Hamiltonian flows provide accurate posterior approximations with significantly reduced runtime compared with competing dynamical-system-based inference methods.