Summary of Trustworthy Classification Through Rank-based Conformal Prediction Sets, by Rui Luo and Zhixin Zhou
Trustworthy Classification through Rank-Based Conformal Prediction Sets
by Rui Luo, Zhixin Zhou
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed conformal prediction method employs a rank-based score function suitable for classification models that predict the order of labels correctly, even if not well-calibrated. The approach constructs prediction sets that achieve the desired coverage rate while managing their size. A theoretical analysis of the expected size of the conformal prediction sets based on the rank distribution of the underlying classifier is provided. Extensive experiments demonstrate that the method outperforms existing techniques on various datasets, providing reliable uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict things is introduced in this paper. It helps machines make decisions by giving them a range of possible answers with how sure they are about each one. This makes it easier for humans to understand how confident the machine is in its answer. The new method works well even when the machine isn’t very good at guessing what the correct answer is. The paper shows that this method is better than other methods on different types of problems, which makes it useful for real-world applications. |
Keywords
* Artificial intelligence * Classification
