Summary of Don’t Label Twice: Quantity Beats Quality When Comparing Binary Classifiers on a Budget, by Florian E. Dorner and Moritz Hardt
Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget
by Florian E. Dorner, Moritz Hardt
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 investigates optimal strategies for allocating a budget of noisy labels to compare the accuracy of two binary classifiers. Contrary to conventional wisdom, it demonstrates that spending the budget on collecting single labels for more samples is the most effective approach. This finding stems from a novel application of Cramér’s theorem and has implications for machine learning benchmark design. The results also provide improved sample size bounds compared to Hoeffding’s bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use a limited number of noisy labels to compare two binary classifiers. Instead of getting many labels that agree or disagree, they show it’s better to get just one label for more samples. This changes the way we design machine learning tests and gives us new rules for how many samples are needed. |
Keywords
* Artificial intelligence * Machine learning
