On Contrastive Learning for Likelihood-free Inference

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Authors

Conor Durkan, Iain Murray, George Papamakarios

Abstract

<p>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 samples drawn from some reference distribution, implicitly learning a density ratio proportional to the likelihood. Another popular class of methods proposes to fit 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.</p>