Summary of Sava: Scalable Learning-agnostic Data Valuation, by Samuel Kessler et al.
SAVA: Scalable Learning-Agnostic Data Valuation
by Samuel Kessler, Tam Le, Vu Nguyen
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 a crucial problem in machine learning: selecting high-quality training data. Researchers often use large, web-scraped datasets, which contain noisy artifacts that affect model performance. To address this issue, the authors formulate a data valuation task, assigning values to data points based on their similarity to a clean validation set. The LAVA algorithm was previously shown to efficiently value training data without relying on model performance. However, it requires the entire dataset as input, limiting its application to larger datasets. To overcome this limitation, the authors propose SAVA, a scalable variant of LAVA that processes batches of data points instead of the entire dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, researchers are trying to figure out how to select the best training data for machine learning models. They found that using big datasets with noisy information can harm model performance. To solve this problem, they developed a method to value each piece of training data based on how similar it is to clean validation data. This method, called LAVA, works well but only works with small datasets. The new algorithm, SAVA, can handle large datasets and still provides good results. |
Keywords
» Artificial intelligence » Machine learning
