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ICML 2016

Pliable Rejection Sampling

Conference Paper Accepted Papers Artificial Intelligence ยท Machine Learning

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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Context

Venue
International Conference on Machine Learning
Archive span
1993-2025
Indexed papers
16471
Paper id
859759291252101226