Summary of On Information-theoretic Measures Of Predictive Uncertainty, by Kajetan Schweighofer et al.
On Information-Theoretic Measures of Predictive Uncertainty
by Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 research paper proposes a fundamental framework for estimating predictive uncertainty in machine learning applications. The authors aim to establish a consensus on measuring predictive uncertainty, which is crucial for high-stakes scenarios where risk hedging is essential. They categorize predictive uncertainty measures based on the predicting model and the approximation of the true predictive distribution, deriving a set of measures that includes both known and newly introduced ones. The proposed framework is evaluated in typical uncertainty estimation settings such as misclassification detection, selective prediction, and out-of-distribution detection. The results show that no single measure is universally effective, but rather depends on the specific setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive uncertainty is important for machine learning because it helps us make better decisions when we’re not sure what will happen. In this paper, researchers developed a new way to think about predictive uncertainty by looking at two things: the model that’s making the prediction and how well it can predict the outcome. They came up with some new ways to measure predictive uncertainty and tested them in different situations. What they found was that there is no one “right” way to do this, but rather it depends on what you’re trying to use the prediction for. |
Keywords
* Artificial intelligence * Machine learning
