Bayesian Pseudo-Coresets via Contrastive Divergence

arXiv:2303.11278v2 Announce Type: replace
Abstract: Bayesian methods provide an elegant framework for estimating parameter posteriors and quantification of uncertainty associated with probabilistic models. However, they often suffer from slow inference times. To address this challenge, Bayesian Pseudo-Coresets (BPC) have emerged as a promising solution. BPC methods aim to create a small synthetic dataset, known as pseudo-coresets, that approximates the posterior inference achieved with the original dataset. This approximation is achieved by optimizing a divergence measure between the true posterior and …

abstract aim arxiv bayesian challenge create cs.ai cs.lg dataset divergence framework however inference quantification small solution synthetic type uncertainty via