arXiv:2604.21407v2 Announce Type: replace Abstract: When approximating an intractable density via variational inference (VI) the variational family is typically chosen as a simple parametric family that very likely does not contain the target. This raises the question: Under which conditions can we recover characteristics of the target despite misspecification? In this work, we extend previous theoretical results on robust VI with location-scale families under target symmetries in two substantial ways: (1) We open them up to a wider range of divergences by providing sufficient conditions for e
This research builds on existing theoretical work in robust variational inference, indicating a continuous effort to improve the reliability and applicability of AI algorithms.
Improved theoretical guarantees for variational inference contribute to more robust and accurate AI models, which is crucial for deployment in sensitive or high-stakes applications.
The conditions under which variational inference can reliably approximate complex densities are being expanded, potentially leading to broader adoption and more trustworthy AI systems.
- · AI researchers
- · Machine learning developers
- · Industries relying on statistical modeling
- · Developers of less robust inference methods
Enhances the theoretical foundation and practical reliability of variational inference.
Leads to the development of more stable and generalizable AI algorithms across various domains.
Accelerates the adoption of advanced AI in fields requiring high certainty and interpretability.
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Read at arXiv cs.LG