Work

Pliable rejection sampling

Year: 2026

Type: preprint

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.

Source: HAL (Le Centre pour la Communication Scientifique Directe)

DOI: 10.48550/arxiv.2604.22385

Authors: Erraqabi, Akram, Valko, Michal, Carpentier, Alexandra, Maillard, Odalric-Ambrym

Institutions: Mila - Quebec Artificial Intelligence Institute, Université de Montréal, University of Potsdam

Language: French

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche

Awards: Extraction and Transfer of Knowledge in Reinforcement Learning, G5937014020

OpenAlex ID: W2403304671

Cites: 18

Cited by: 3

Related to: 10