Summary of Vtrust: Controllable Value Function Based Subset Selection For Data-centric Trustworthy Ai, by Soumi Das et al.
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI
by Soumi Das, Shubhadip Nag, Shreyyash Sharma, Suparna Bhattacharya, Sourangshu Bhattacharya
First submitted to arxiv on: 8 Mar 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 The proposed framework, VTruST, enables users to control the trade-offs between fairness, robustness, and accuracy in constructed training datasets for trustworthy AI. To achieve this, a novel online sparse approximation algorithm is designed, building upon Orthogonal Matching Pursuit (OMP). Experimental results demonstrate VTruST’s superiority on social, image, and scientific datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a way to make AI more reliable by controlling the data used to train models. It does this by selecting only the most important training data points. This approach is shown to work well on different types of datasets, including those related to social media, images, and science. |
