mlsanityData to 7 Oct 2026

Pliable rejection sampling

arXiv 2604.22385 ↗First posted 24 Apr 2026stat.ML cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 24 Apr 2026.

Builds on

3 of 20 references.

References · 3

  1. 2013Expectation Propagation for approximate Bayesian inferencecited by 172
  2. 2014A* Samplingcited by 150
  3. 2014The entropic barrier: a simple and optimal universal self-concordant barriercited by 15

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