Adversarial Likelihood-Free Inference on Black-Box Generator

• URL: http://arxiv.org/abs/2004.05803v2
• Date: Thu, 11 Jun 2020 14:50:27 GMT
• Title: Adversarial Likelihood-Free Inference on Black-Box Generator
• Authors: Dongjun Kim, Weonyoung Joo, Seungjae Shin, Kyungwoo Song, Il-Chul Moon
• Abstract summary: Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution. This paper analyzes the theoretic limitations of the proposal distribution approach. We introduce a new algorithm, Adversarial Likelihood-Free Inference (ALFI), to mitigate the analyzed limitations.
• Score: 26.122782625918163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input parameter estimation of black-box generators. While previous works on likelihood-free inference introduces an implicit proposal distribution on the generator input, this paper analyzes theoretic limitations of the proposal distribution approach. On top of that, we introduce a new algorithm, Adversarial Likelihood-Free Inference (ALFI), to mitigate the analyzed limitations, so ALFI is able to find the posterior distribution on the input parameter for black-box generative models. We experimented ALFI with diverse simulation models as well as pre-trained statistical models, and we identified that ALFI achieves the best parameter estimation accuracy with a limited simulation budget.

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