Embarrassingly parallel MCMC using deep invertible transformations

Diego Mesquita, Paul Blomstedt, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)
50 Downloads (Pure)


While MCMC methods have become a main work-horse for Bayesian inference, scaling them to large distributed datasets is still a challenge. Embarrassingly parallel MCMC strategies take a divide-and-conquer stance to achieve this by writing the target posterior as a product of subposteriors, running MCMC for each of them in parallel and subsequently combining the results. The challenge then lies in devising efficient aggregation strategies. Current strategies tradeoff between approximation quality, and costs of communication and computation. In this work, we introduce a novel method that addresses these issues simultaneously. Our key insight is to introduce a deep invertible transformation to approximate each of the subposteriors. These approximations can be made accurate even for complex distributions and serve as intermediate representations, keeping the total communication cost limited. Moreover, they enable us to sample from the product of the subposteriors using an efficient and stable importance sampling scheme. We demonstrate that the approach outperforms available state-of-the-art methods in a range of challenging scenarios, including high-dimensional and heterogeneous subposteriors.

Original languageEnglish
Title of host publicationProceedings of The 35th Uncertainty in Artificial Intelligence Conference
ISBN (Print)9781510891562
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventConference on Uncertainty in Artificial Intelligence - Tel Aviv, Israel
Duration: 22 Jul 201925 Jul 2019
Conference number: 35

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)1938-7228


ConferenceConference on Uncertainty in Artificial Intelligence
Abbreviated titleUAI
CityTel Aviv


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