  Bayesian coresets have emerged as a promising approach for implementing
scalable Bayesian inference. The Bayesian coreset problem involves selecting a
(weighted) subset of the data samples, such that the posterior inference using
the selected subset closely approximates the posterior inference using the full
dataset. This manuscript revisits Bayesian coresets through the lens of
sparsity constrained optimization. Leveraging recent advances in accelerated
optimization methods, we propose and analyze a novel algorithm for coreset
selection. We provide explicit convergence rate guarantees and present an
empirical evaluation on a variety of benchmark datasets to highlight our
proposed algorithm's superior performance compared to state-of-the-art on speed
and accuracy.
