提出自适应变分推断方法，提升复杂模型下的推断精度
Adaptive Variational Inference in Probabilistic Graphical Models: Beyond Bethe, Tree-Reweighted, and Convex Free Energies
- 通过调整模型参数或熵近似方式实现自由能逼近的动态优化
- 在复杂高交互模型上显著优于Bethe、树重加权等传统方法
- 适合需要高精度推断的复杂概率图模型应用场景
概率图模型中的变分推断旨在近似边缘分布和分区函数等基本量。主流方法包括Bethe近似、树重加权及其它凸自由能近似。这些方法虽高效，但在模型复杂且高度交互时可能失效。本文分析两类可涵盖上述方法的近似框架：一是模型参数变化，二是熵近似变化。讨论了各自优缺点，并据此推导出理想自由能近似应如何构建。基于此，提出能自动适配给定模型的近似方法，并在一系列困难问题上验证其有效性。
原文摘要 · Abstract (English)
Variational inference in probabilistic graphical models aims to approximate fundamental quantities such as marginal distributions and the partition function. Popular approaches are the Bethe approximation, tree-reweighted, and other types of convex free energies. These approximations are efficient but can fail if the model is complex and highly interactive. In this work, we analyze two classes of approximations that include the above methods as special cases: first, if the model parameters are changed; and second, if the entropy approximation is changed. We discuss benefits and drawbacks of either approach, and deduce from this analysis how a free energy approximation should ideally be constructed. Based on our observations, we propose approximations that automatically adapt to a given model and demonstrate their effectiveness for a range of difficult problems.
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