@inproceedings{erraqabi2016pliable,
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.},
author = {Erraqabi, Akram and Valko, Michal and Carpentier, Alexandra and Maillard, Odalric-Ambrym},
booktitle = {International Conference on Machine Learning},
title = {{Pliable rejection sampling}},
year = {2016}
}