Summary of Multiclass Learning From Noisy Labels For Non-decomposable Performance Measures, by Mingyuan Zhang et al.
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures
by Mingyuan Zhang, Shivani Agarwal
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Machine learning educators can learn about a recent interest in learning good classifiers from noisy data labels. The paper focuses on non-decomposable performance measures, such as H-mean, Q-mean, and G-mean, which are essential for many machine learning problems. The authors design algorithms to learn from noisy labels for these multiclass non-decomposable performance measures, building on the Frank-Wolfe and Bisection methods. The algorithms provide regret bounds, ensuring they converge to optimal performance despite being trained on noisy data. Experiments demonstrate the effectiveness of the algorithms in handling label noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learning is about finding good answers from messy information. Scientists want to know how to teach machines to make correct decisions even when they have bad training examples. This paper helps with that by creating new ways for machines to learn from wrong or noisy data labels. It works on special types of math problems that require different kinds of scores, like H-mean and G-mean. The scientists use old methods to create new ones that can handle noise in the data. They show how their new algorithms work well in practice. |
Keywords
* Artificial intelligence * Machine learning
