Symmetry Guarantees Statistic Recovery in Variational Inference
Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. As the variational family cannot typically represent the target exactly, guarantees on the quality of the resulting approximation are crucial for understanding which of its properties VI can faithfully capture. Recent work has identified instances in which symmetries of the target and the variational family enable the recovery of certain statistics, even under model misspecification. However, these guarantees are inherently problem-specific and offer little insight into the fundamental mechanism by which symmetry...
| Authors | Daniel Marks, Dario Paccagnan, Mark van der Wilk |
|---|---|
| Primary category | stat.ML |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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