Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational Inference
arXiv:2601.14855v3 Announce Type: replace Abstract: Black-box variational inference (BBVI) with Gaussian mixture families offers a flexible approach for approximating complex posterior distributions without requiring gradients of the target density. However, standard numerical optimization methods often suffer from instability and inefficiency. We develop a stable and efficient framework that combines three key components: (1) affine-invariant preconditioning via natural gradient formulations, (2) an exponential integrator that unconditionally preserves the positive definiteness of covariance
The paper represents an incremental but important step in refining Black-box variational inference (BBVI) techniques, driven by the ongoing need for more stable and efficient methods in AI research.
Improved stability and efficiency in BBVI with Gaussian mixtures can lead to more robust and powerful AI models, particularly in applications requiring complex posterior distribution approximations without direct density gradients.
The proposed framework addresses instability and inefficiency issues in a common AI inference method, potentially broadening the applicability of sophisticated variational inference techniques in practical machine learning.
- · AI researchers
- · Machine learning developers
- · Sectors using complex probabilistic models
- · Less efficient or unstable inference methods
Increased adoption and reliability of variational inference methods for complex AI tasks.
Faster development and deployment of AI systems requiring advanced probabilistic modeling due to more stable inference.
Potential for new AI applications in fields like scientific discovery or finance, where robust approximation of complex uncertainties is critical.
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Read at arXiv cs.LG