Pliable rejection sampling
Machine Learning
2026-04-27 v1 Machine Learning
Abstract
Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive rejection sampling methods either work only for very specific distributions or without performance guarantees. In this paper, we present pliable rejection sampling (PRS), a new approach to rejection sampling, where we learn the sampling proposal using a kernel estimator. Since our method builds on rejection sampling, the samples obtained are with high probability i.i.d. and distributed according to f. Moreover, PRS comes with a guarantee on the number of accepted samples.
Cite
@article{arxiv.2604.22385,
title = {Pliable rejection sampling},
author = {Akram Erraqabi and Michal Valko and Alexandra Carpentier and Odalric-Ambrym Maillard},
journal= {arXiv preprint arXiv:2604.22385},
year = {2026}
}
Comments
In ICML 2016