Summary of Batch, Match, and Patch: Low-rank Approximations For Score-based Variational Inference, by Chirag Modi et al.
Batch, match, and patch: low-rank approximations for score-based variational inference
by Chirag Modi, Diana Cai, Lawrence K. Saul
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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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 The paper extends the batch-and-match framework for score-based black-box variational inference (BBVI) to handle high-dimensional problems where storing full covariance matrices is impractical. Unlike traditional BBVI methods, BaM uses specialized updates to match target density scores and Gaussian approximations. The authors integrate these updates with a compact parameterization of full covariance matrices using factor analysis-inspired “patches” that project updated matrices into diagonal plus low-rank forms. Synthetic and real-world experiments demonstrate the effectiveness of this approach in high-dimensional inference tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, researchers are working on making it easier to analyze complex data by developing a new way to do something called black-box variational inference. This method helps computers learn about patterns in big datasets without having to understand what’s going on inside those patterns. The authors of this paper improved this method so it can handle really large datasets with many features. They tested their approach and found that it worked well for finding patterns in fake data and real-world problems. |
Keywords
* Artificial intelligence * Inference
