Summary of Geometric Median (gm) Matching For Robust Data Pruning, by Anish Acharya et al.
Geometric Median (GM) Matching for Robust Data Pruning
by Anish Acharya, Inderjit S Dhillon, Sujay Sanghavi
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to robust data pruning is proposed, leveraging Geometric Median (GM) Matching as a greedy algorithm. GM Matching yields a k-subset that approximates the geometric median of a potentially noisy dataset, offering improved scaling over uniform sampling and optimal breakdown point even under arbitrary corruption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In plain English, this paper develops a new way to clean up large datasets by removing noise while keeping important information. The method is designed to work well even when there’s a lot of noise in the data. It does this by finding a small group of points that are close to the average point in the dataset. |
Keywords
» Artificial intelligence » Pruning
