Title: Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent,
Abstract: Coherent uncertainty quantification is a key strength of Bayesian methods.
But modern algorithms for approximate Bayesian posterior inference often
sacrifice accurate posterior uncertainty estimation in the pursuit of
scalability. This work shows that previous Bayesian coreset construction
algorithms---which build a small, weighted subset of the data that approximates
the full dataset---are no exception. We demonstrate that these algorithms scale
the coreset log-likelihood suboptimally, resulting in underestimated posterior
uncertainty. To address this shortcoming, we develop greedy iterative geodesic
ascent (GIGA), a novel algorithm for Bayesian coreset construction that scales
the coreset log-likelihood optimally. GIGA provides geometric decay in
posterior approximation error as a function of coreset size, and maintains the
fast running time of its predecessors. The paper concludes with validation of
GIGA on both synthetic and real datasets, demonstrating that it reduces
posterior approximation error by orders of magnitude compared with previous
coreset constructions.