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.

该研究表明，先前的贝叶斯核心子集构建算法在估计后验不确定性时存在不足；因此，提出了一种新的贝叶斯核心子集构建算法 ——GIGA，其优化核心对数似然度以实现后验不确定性的准确估计并且同时保持较快的计算速度。最后在合成和实际数据集上验证了 GIGA 的有效性。