Structured Nonparametric Variational Inference for Dependent Latent Modeling
Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models. In this paper, we propose Structured Nonparametric Variational Inference (SN-VI), a novel framework for modeling complex dependencies among latent variables in posterior approximation, leveraging multivariate spline techniques. Unlike traditional methods that rely on the mean-field assumption, SN-VI preserves intricate latent variable dependencies, providing a flexible and accurate approximation of posteriors with arbitrary shapes. We establish rigorous theoretical guarantees, including the derivation of the ...
| Authors | Yuda Shao, Zhiling Gu, Shan Yu |
|---|---|
| Primary category | stat.ML |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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