Summary of From Risk to Uncertainty: Generating Predictive Uncertainty Measures Via Bayesian Estimation, by Nikita Kotelevskii et al.
From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
by Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč, Éric Moulines, Maxim Panov
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel framework for decomposing statistical pointwise risk into components, shedding light on the relationships between various measures of predictive uncertainty. By separating aleatoric and epistemic uncertainties, the approach enables the generation of distinct predictive uncertainty measures using Bayesian methods as an approximation. This framework has implications for model evaluation and selection in machine learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how uncertain our predictions are. Imagine you’re trying to predict what will happen tomorrow based on data from yesterday. There’s some natural variability in the data, like weather being sunny or rainy. That’s aleatoric uncertainty. Then there’s epistemic uncertainty, which comes from using a model that might not be perfect. The paper shows how these two types of uncertainty are connected and provides a way to measure them separately. |
Keywords
* Artificial intelligence * Machine learning
