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June 20, 2026IEEE Transactions on Pattern Analysis and Machine IntelligenceOpen Access

Adaptive Variational Inference: Beyond Bethe, Tree-Reweighted, and Convex Free Energies

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Authors

HLHarald LeisenbergerFPFranz Pernkopf

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Overview

Randomized trial analyzes approximation methods in graphical models, suggesting optimal parameter settings.

Key Points

  • This work aims to enhance variational inference methods for complex graphical models by introducing new approximation strategies.
  • Analyzed two classes of approximations derived from energy and entropy modifications.
  • Introduced adaptive methods ADAPT-$c$ and ADAPT-$eta$ to automatically determine optimal parameter settings.
  • Demonstrated effectiveness through experiments on pairwise binary graphical models.
  • Significantly improved accuracy in approximating marginal distributions compared to traditional methods.
  • Enhanced partition function estimates under complex model interactions.
  • Demonstrated robust performance across different model complexities.

Cite This Study

Leisenberger et al. (2026) studied this question.

synapsesocial.com/papers/6a362de1db0793dc1a535e04https://doi.org/10.1109/tpami.2026.3704719
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