Summary of Learning From Concealed Labels, by Zhongnian Li et al.
Learning from Concealed Labels
by Zhongnian Li, Meng Wei, Peng Ying, Tongfeng Sun, Xinzheng Xu
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a novel approach to learning from concealed labels for multi-class classification, specifically designed to protect individual privacy in real-world scenarios where sensitive data is involved. The authors propose an unbiased estimator that can be established from concealed data under mild assumptions, allowing for accurate classification of instances based on insensitive labels while also recognizing sensitive labels. The paper demonstrates the effectiveness of this method through experiments using synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed approach helps protect individual privacy by concealing sensitive labels during label collection, preventing sensitive information from being revealed. The authors introduce an unbiased estimator that can accurately classify instances based on insensitive labels while also recognizing sensitive labels. This method has the potential to significantly improve privacy protection in real-world scenarios where sensitive data is involved. |
Keywords
» Artificial intelligence » Classification
