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Generative Adversarial Network (GAN) can be viewed as an implicit estimatorof a data distribution, and this perspective motivates using the adversarialconcept in the true input parameter estimation of black-box generators. Whileprevious works on likelihood-free inference introduces an implicit proposaldistribution on the generator input, this paper analyzes theoretic limitationsof the proposal distribution approach. On top of that, we introduce a newalgorithm, Adversarial Likelihood-Free Inference (ALFI), to mitigate theanalyzed limitations, so ALFI is able to find the posterior distribution on theinput parameter for black-box generative models. We experimented ALFI withdiverse simulation models as well as pre-trained statistical models, and weidentified that ALFI achieves the best parameter estimation accuracy with alimited simulation budget.

Adversarial Likelihood-Free Inference on Black-Box Generator