两篇新论文探讨了目标分布中的对称性如何在变分推理过程中保证某些统计量的恢复，即使所选的变分族被错误指定。该研究为对称性诱导的统计恢复提供了一个通用理论，统一了现有结果并将其扩展到球体分布等新场景。这些发现为变分推理的基本机制提供了见解，并为选择变分族和参数以确保目标属性的准确近似提供了指导。 AI
影响 为变分推理提供理论保证，有可能提高复杂模型中统计恢复的可靠性。
排序理由 两篇学术论文发表在 arXiv 上，详细介绍了变分推理的理论进展。
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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 t…
Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. As the variational family cannot typically represent the target exactly, guarantees on the quality of…