Wormhole Hamiltonian Monte Carlo

Shiwei Lan, Jeffrey Streets, Babak Shahbaba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

In machine learning and statistics, probabilistic inference involving multimodal distributions is quite difficult. This is especially true in high dimensional problems, where most existing algorithms cannot easily move from one mode to another. To address this issue, we propose a novel Bayesian inference approach based on Markov Chain Monte Carlo. Our method can effectively sample from multimodal distributions, especially when the dimension is high and the modes are isolated. To this end, it exploits and modifies the Riemannian geometric properties of the target distribution to create wormholes connecting modes in order to facilitate moving between them. Further, our proposed method uses the regeneration technique in order to adapt the algorithm by identifying new modes and updating the network of wormholes without affecting the stationary distribution. To find new modes, as opposed to rediscovering those previously identified, we employ a novel mode searching algorithm that explores a residual energy function obtained by subtracting an approximate Gaussian mixture density (based on previously discovered modes) from the target density function.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages1953-1959
Number of pages7
ISBN (Electronic)9781577356790
StatePublished - 2014
Externally publishedYes
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: Jul 27 2014Jul 31 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume3

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period7/27/147/31/14

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Wormhole Hamiltonian Monte Carlo'. Together they form a unique fingerprint.

Cite this