Jia-Fang Song

Neural Variational Cut Posteriors without Upstream Data

This work proposes a modular and provably accurate cut-Bayes approach requiring no access to upstream data or model, and establishes, to the knowledge, first results on uniform Kullback-Leibler approximation rates of conditional distributions by common flow classes, yielding widely applicable fixed-data error rates for...