Pliable rejection sampling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866910162483150848 |
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| author | Erraqabi, Akram Valko, Michal Carpentier, Alexandra Maillard, Odalric-Ambrym |
| author_facet | Erraqabi, Akram Valko, Michal Carpentier, Alexandra Maillard, Odalric-Ambrym |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22385 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pliable rejection sampling Erraqabi, Akram Valko, Michal Carpentier, Alexandra Maillard, Odalric-Ambrym Machine Learning 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. |
| title | Pliable rejection sampling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.22385 |