Summary of Robust Random Graph Matching in Dense Graphs Via Vector Approximate Message Passing, by Zhangsong Li
Robust random graph matching in dense graphs via vector approximate message passing
by Zhangsong Li
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Probability (math.PR); 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 tackles the challenge of matching recovery between two correlated Gaussian Wigner matrices, with a focus on a robust version that can withstand adversarial perturbations. The authors propose a vector-approximate message passing (vector-AMP) algorithm to solve this problem efficiently in polynomial time. To achieve this, they require a non-vanishing correlation constant and a small perturbation size, which is proportional to the logarithm of the matrix size. The proposed method has implications for statistical inference and machine learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding matching patterns between two related datasets that are affected by random noise and intentional disruptions. Researchers want to develop an algorithm that can efficiently identify these patterns despite the interference. They create a new approach called vector-AMP, which works well if the relationship between the two datasets remains strong and the disruptions are not too severe. |
Keywords
» Artificial intelligence » Inference » Machine learning
