Summary of Efficient Pac Learning Of Halfspaces with Constant Malicious Noise Rate, by Jie Shen
Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate
by Jie Shen
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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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 paper explores the problem of learning halfspaces in the presence of malicious noise. In this setting, an adversary can corrupt both instances and labels of training samples. The authors show that it is possible to achieve constant noise tolerance by minimizing a reweighted hinge loss, given certain conditions on the target error rate under distributional assumptions or large-margin conditions. The key contributions include an efficient algorithm for finding weights to control gradient deterioration from corrupted samples and a new analysis on the robustness of the hinge loss with these weights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning algorithms more resistant to bad data. In this case, the “bad data” can be either the examples used to train the algorithm or the labels that tell the algorithm what’s right and wrong. The researchers show how to make an important type of algorithm (called halfspaces) work well even when some of the training data is corrupted. They do this by using a special kind of loss function and analyzing it in a new way. |
Keywords
» Artificial intelligence » Hinge loss » Loss function » Machine learning
