Two new papers explore how symmetries in target distributions can guarantee the recovery of certain statistics during variational inference, even when the chosen variational family is misspecified. The research provides a general theory for symmetry-induced statistic recovery, unifying existing results and extending them to new settings like distributions on the sphere. These findings offer insights into the fundamental mechanisms of variational inference and provide guidelines for selecting variational families and parameters to ensure accurate approximation of target properties. AI
影响 Provides theoretical guarantees for variational inference, potentially improving the reliability of statistical recovery in complex models.
排序理由 Two academic papers published on arXiv detailing theoretical advancements in variational inference.
- arXiv
- location-scale families
- von Mises-Fisher families
- Kullback-Leibler divergence
- Variational Inference
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