SCIENCE@home

Annealed Flow Transport Monte Carlo

M. Arbel,A. G. Matthews,A. Doucet

Published 2021 in International Conference on Machine Learning

ABSTRACT

Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC) extensions are state-of-the-art methods for estimating normalizing constants of probability distributions. We propose here a novel Monte Carlo algorithm, Annealed Flow Transport (AFT), that builds upon AIS and SMC and combines them with normalizing flows (NFs) for improved performance. This method transports a set of particles using not only importance sampling (IS), Markov chain Monte Carlo (MCMC) and resampling steps as in SMC, but also relies on NFs which are learned sequentially to push particles towards the successive annealed targets. We provide limit theorems for the resulting Monte Carlo estimates of the normalizing constant and expectations with respect to the target distribution. Additionally, we show that a continuous-time scaling limit of the population version of AFT is given by a Feynman–Kac measure which simplifies to the law of a controlled diffusion for expressive NFs. We demonstrate experimentally the benefits and limitations of our methodology on a variety of applications.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    International Conference on Machine Learning

  • Publication date

    2021-02-15

  • Fields of study

    Mathematics, Physics, Computer Science

  • Identifiers
    arXiv 2102.07501
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-86 of 86 references · Page 1 of 1

CITED BY

Showing 1-96 of 96 citing papers · Page 1 of 1