# Pliable rejection sampling

arXiv: https://arxiv.org/abs/2604.22385 · HTML: https://mlsanity.com/paper/2604.22385/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Expectation Propagation for approximate Bayesian inference](https://mlsanity.com/paper/1301.2294/) (2013)
- [A* Sampling](https://mlsanity.com/paper/1411.0030/) (2014)
- [The entropic barrier: a simple and optimal universal self-concordant barrier](https://mlsanity.com/paper/1412.1587/) (2014)

## Abstract (arXiv)

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.