Summary of Extending Mean-field Variational Inference Via Entropic Regularization: Theory and Computation, by Bohan Wu et al.
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
by Bohan Wu, David Blei
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 a novel method called -variational inference (-VI) that extends naive mean field via entropic regularization. The approach has connections to the optimal transport problem and leverages the Sinkhorn algorithm for efficient computation. The authors demonstrate that -VI effectively recovers true posterior dependencies, which are downweighted by a regularization parameter. They also analyze the impact of dimensionality on approximation accuracy and computational considerations, providing a statistical-computational trade-off characterization. Additionally, they establish frequentist properties, including consistency, asymptotic normality, high-dimensional asymptotics, algorithmic stability, and polynomial-time approximate inference criteria. Finally, they show that -VI outperforms mean-field variational inference on simulated and real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to make predictions from complex data using something called “variational inference”. It’s like trying to guess what a person might say next in a conversation by looking at patterns in their previous words. The method is special because it uses an algorithm that can quickly find the best solution, even when there are lots of variables involved. The researchers tested this new approach and found that it works well on both fake and real data. It’s also better than another popular method for making predictions. |
Keywords
» Artificial intelligence » Inference » Regularization
