Predicate exchange: Inference with declarative knowledge

Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar Lezama, Rajesh Ranganath

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


Programming languages allow us to express complex predicates, but existing inference methods are unable to condition probabilistic models on most of them. To support a broader class of predicates, we develop an inference procedure called predicate exchange, which softens predicates. A soft predicate quantifies the extent to which values of model variables are consistent with its hard counterpart. We substitute the likelihood term in the Bayesian posterior with a soft predicate, and develop a variant of replica exchange MCMC to draw posterior samples. We implement predicate exchange as a language agnostic tool which performs a nonstandard execution of a probabilistic program. We demonstrate the approach on sequence models of health and inverse rendering.

Original languageEnglish (US)
Title of host publication36th International Conference on Machine Learning, ICML 2019
PublisherInternational Machine Learning Society (IMLS)
Number of pages10
ISBN (Electronic)9781510886988
StatePublished - 2019
Event36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States
Duration: Jun 9 2019Jun 15 2019

Publication series

Name36th International Conference on Machine Learning, ICML 2019


Conference36th International Conference on Machine Learning, ICML 2019
Country/TerritoryUnited States
CityLong Beach

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Human-Computer Interaction


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