Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry
arXiv:2511.01064v3 Announce Type: replace-cross Abstract: Variational inference (VI) approximates a target density $p$ by the best match $q$ in a family of tractable distributions. The best variational approximation is found by minimizing a divergence between distributions, $D(p||q)$, and several divergences have been proposed as objective functions for VI, with different choices leading to different approximations. We show that even when these divergences have different minimizers, the resulting approximations all abide by certain symmetry-matching principles. Specifically, our results hold f
This paper's publication demonstrates ongoing, fundamental research in variational inference, a core component of modern probabilistic machine learning, refining its theoretical underpinnings.
Improved theoretical guarantees for variational inference lead to more robust, predictable, and potentially more efficient AI models, which is crucial for deploying AI in critical applications.
The understanding of how different variational inference approximations behave under symmetry conditions is advanced, potentially guiding better practical choices in model design.
- · AI researchers
- · Machine learning framework developers
- · Industries relying on probabilistic AI models
- · Researchers using suboptimal VI approaches
- · Developers with ad-hoc AI solutions
Fundamental theoretical work like this improves the reliability and interpretability of AI models.
More reliable AI models could accelerate adoption in high-stakes domains like healthcare or finance, where certainty and explainability are paramount.
The enhanced trustworthiness of AI systems stemming from such theoretical advances could ultimately broaden public and regulatory acceptance of AI technologies.
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Read at arXiv cs.LG