A Particle Algorithm for Mean-Field Variational Inference

Qiang Du; Kaizheng Wang; Edith Zhang; Chenyang Zhong

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arXiv · 2412.20385

A Particle Algorithm for Mean-Field Variational Inference

Qiang Du·Kaizheng Wang·Edith Zhang·Chenyang Zhong

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Abstract

Variational inference is a fast and scalable alternative to Markov chain Monte Carlo and has been widely applied to posterior inference tasks in statistics and machine learning. A traditional approach for implementing mean-field variational inference (MFVI) is coordinate ascent variational inference (CAVI), which relies crucially on parametric assumptions on complete conditionals. We introduce a novel particle-based algorithm for MFVI, named PArticle VI (PAVI), for nonparametric mean-field approximation. We obtain non-asymptotic error bounds for our algorithm. To our knowledge, this is the first end-to-end guarantee for particle-based MFVI.

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Qiang Du, Kaizheng Wang, Edith Zhang, Chenyang Zhong. 2024-12-29. A Particle Algorithm for Mean-Field Variational Inference. https://arxiv.org/abs/2412.20385

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