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On Contrastive Learning for Likelihood-free Inference

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Original languageEnglish
Title of host publicationProceedings of the 37th International Conference on Machine Learning
Number of pages15
Publication statusAccepted/In press - 31 May 2020
EventThirty-seventh International Conference on Machine Learning 2020 - Virtual conference
Duration: 13 Jul 202018 Jul 2020

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


ConferenceThirty-seventh International Conference on Machine Learning 2020
Abbreviated titleICML 2020
CityVirtual conference
Internet address


Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sampled from some reference distribution, which implicitly learns a density ratio proportional to the likelihood. Another popular class of methods fits a conditional distribution to the parameter posterior directly, and a particular recent variant allows for the use of flexible neural density estimators for this task. In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be run and compared.


Thirty-seventh International Conference on Machine Learning 2020


Virtual conference

Event: Conference

ID: 159928363