Pliable Rejection Sampling

Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard

ICML 2016 pp. 2121-2129

/icml/2016/erraqabi2016icml-pliable/

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.

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Cite

Text

Erraqabi et al. "Pliable Rejection Sampling." International Conference on Machine Learning, 2016.

Markdown

[Erraqabi et al. "Pliable Rejection Sampling." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/erraqabi2016icml-pliable/)

BibTeX

@inproceedings{erraqabi2016icml-pliable,
  title     = {{Pliable Rejection Sampling}},
  author    = {Erraqabi, Akram and Valko, Michal and Carpentier, Alexandra and Maillard, Odalric},
  booktitle = {International Conference on Machine Learning},
  year      = {2016},
  pages     = {2121-2129},
  volume    = {48},
  url       = {https://mlanthology.org/icml/2016/erraqabi2016icml-pliable/}
}