Summary of Label-wise Aleatoric and Epistemic Uncertainty Quantification, by Yusuf Sale et al.
Label-wise Aleatoric and Epistemic Uncertainty Quantification
by Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier
First submitted to arxiv on: 4 Jun 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 This paper introduces a new approach to measuring uncertainty in classification tasks by breaking it down into individual class levels. This allows for more informed decision-making and a better understanding of where uncertainty comes from. The method also provides a way to define different types of uncertainty, such as total, aleatoric, and epistemic, using measures like variance rather than just entropy. By addressing some limitations in existing methods, this approach can improve the accuracy of uncertainty quantification. The paper demonstrates its effectiveness through experiments on various benchmark datasets, including medical applications where accurate uncertainty estimation is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to measure how certain we are about something. Normally, when we’re trying to figure out if someone has a disease or not, we look at the overall chance of being correct. But what if you want to know how sure you are that it’s one specific type of disease? This paper shows you how to do just that by looking at each possible outcome separately. It also helps us understand where our uncertainty is coming from and gives us a way to describe different kinds of uncertainty. By using this method, we can make better decisions and be more confident in our answers. |
Keywords
» Artificial intelligence » Classification
