Summary of Data Distribution Valuation, by Xinyi Xu et al.
Data Distribution Valuation
by Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti
First submitted to arxiv on: 6 Oct 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 A novel data valuation approach is proposed to assess the value of data distributions from which discrete datasets were sampled. This is crucial in applications like pricing data in marketplaces, where buyers may only observe small preview samples from different vendors to decide which data distribution is most valuable. The authors develop a maximum mean discrepancy (MMD)-based method that enables theoretically principled and actionable policies for comparing data distributions from samples. Empirical evaluations on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression) demonstrate the effectiveness of this method in identifying valuable data distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re buying data from different vendors to use in a project. You only get to see a small preview sample from each vendor, so how do you decide which one to buy? A team of researchers has developed a new way to value these data distributions based on just the samples they provide. They tested their method on real-world datasets and found it works well for identifying valuable data. |
Keywords
» Artificial intelligence » Classification » Regression
