Annealed Flow Transport Monte Carlo - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Annealed Flow Transport Monte Carlo

Résumé

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.
Fichier principal
Vignette du fichier
2102.07501 (2).pdf (1.06 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03455478 , version 1 (29-11-2021)

Identifiants

  • HAL Id : hal-03455478 , version 1

Citer

Michael Arbel, Alexander G D G Matthews, Arnaud Doucet. Annealed Flow Transport Monte Carlo. ICML 2021 - 38th International Conference on Machine Learning, Jul 2021, Online, France. pp.1-70. ⟨hal-03455478⟩
69 Consultations
69 Téléchargements

Partager

Gmail Facebook X LinkedIn More