Summary of Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules, by Paul Hofman et al.
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules
by Paul Hofman, Yusuf Sale, Eyke Hüllermeier
First submitted to arxiv on: 18 Apr 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 In this paper, the authors propose novel methods for quantifying uncertainty in machine learning models, which is crucial for safety-critical applications. They develop proper scoring rules that incentivize learners to predict true probabilities. The approach assumes two common representations of epistemic uncertainty: credal sets and second-order distributions. The framework bridges these representations and provides a formal justification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to measure how certain or uncertain machine learning models are. This is important because some decisions, like self-driving cars, need to be very accurate and safe. The authors create new ways to calculate uncertainty using special formulas called proper scoring rules. These rules make the model predict true probabilities better. They also show how two different ways of representing uncertainty can work together. |
Keywords
» Artificial intelligence » Machine learning
