Summary of Weshap: Weak Supervision Source Evaluation with Shapley Values, by Naiqing Guan and Nick Koudas
WeShap: Weak Supervision Source Evaluation with Shapley Values
by Naiqing Guan, Nick Koudas
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 addresses the challenge of efficient data annotation in machine learning model training by introducing the Programmatic Weak Supervision (PWS) pipeline. The PWS pipeline leverages multiple weak supervision sources to automatically label data, reducing the annotation time. To ensure the accuracy of PWS, a robust and efficient metric is necessary for evaluating the contributions of these weak supervision sources. This evaluation metric plays a crucial role in understanding the performance and behavior of the PWS pipeline and enabling corrective measures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper focuses on speeding up the data annotation process by using multiple weak supervision sources to automatically label data. The authors aim to develop a robust metric for evaluating the accuracy of this approach, which is essential for improving the performance of machine learning models. |
Keywords
* Artificial intelligence * Machine learning
