Summary of Differentiable Annealed Importance Sampling Minimizes the Symmetrized Kullback-leibler Divergence Between Initial and Target Distribution, by Johannes Zenn and Robert Bamler
Differentiable Annealed Importance Sampling Minimizes The Symmetrized Kullback-Leibler Divergence Between Initial and Target Distribution
by Johannes Zenn, Robert Bamler
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to optimization called Differentiable Annealed Importance Sampling (DAIS). Building upon previous work by Geffner & Domke (2021) and Zhang et al. (2021), DAIS allows for optimizing over the initial distribution of AIS. The authors demonstrate that, in the limit of many transitions, DAIS minimizes the symmetrized Kullback-Leibler divergence between the initial and target distribution. This is reminiscent of variational inference (VI) where the initial distribution serves as a parametric fit to an intractable target distribution. Empirical evaluations on synthetic and real-world data reveal that the initial distribution often provides more accurate uncertainty estimates compared to VI, importance weighted VI, and Markovian score climbing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DAIS is a new way of optimizing things. It uses something called annealed importance sampling (AIS) and makes it possible to optimize over the initial distribution. The authors show that DAIS can be seen as a type of variational inference (VI), where you try to find a simpler distribution that matches the real one. They tested this on fake and real data and found that it often gives better results than other methods like VI, importance weighted VI, or Markovian score climbing. |
Keywords
» Artificial intelligence » Inference » Optimization
