Revisiting Unbiased Implicit Variational Inference

• URL: http://arxiv.org/abs/2506.03839v1
• Date: Wed, 04 Jun 2025 11:16:58 GMT
• Title: Revisiting Unbiased Implicit Variational Inference
• Authors: Tobias Pielok, Bernd Bischl, David Rügamer,
• Abstract summary: We show that unbiased implicit variational inference (UIVI) can be effectively replaced via importance sampling and the optimal proposal distribution can be learned stably.<n>Our refined approach demonstrates superior performance or parity with state-of-the-art methods on established SIVI benchmarks.
• Score: 12.300415631357406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have witnessed growing interest in semi-implicit variational inference (SIVI) methods due to their ability to rapidly generate samples from complex distributions. However, since the likelihood of these samples is non-trivial to estimate in high dimensions, current research focuses on finding effective SIVI training routines. Although unbiased implicit variational inference (UIVI) has largely been dismissed as imprecise and computationally prohibitive because of its inner MCMC loop, we revisit this method and show that UIVI's MCMC loop can be effectively replaced via importance sampling and the optimal proposal distribution can be learned stably by minimizing an expected forward Kullback-Leibler divergence without bias. Our refined approach demonstrates superior performance or parity with state-of-the-art methods on established SIVI benchmarks.

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