Summary of Provable Uncertainty Decomposition Via Higher-order Calibration, by Gustaf Ahdritz et al.
Provable Uncertainty Decomposition via Higher-Order Calibration
by Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan, Charlotte Peale, Udi Wieder
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 A novel approach to decomposing predictive uncertainty in machine learning models is presented, providing a principled method for separating aleatoric (data-driven) and epistemic (model-driven) components with explicit semantics. This method is based on the concept of higher-order calibration, which generalizes traditional calibration to higher-order predictors that predict mixtures over label distributions. The proposed approach provides formal guarantees for measuring and achieving higher-order calibration using access to k-snapshots, allowing for the estimation of aleatoric uncertainty without assumptions on the real-world data distribution. This method is applicable to existing higher-order predictors like Bayesian and ensemble models, providing a natural evaluation metric. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive uncertainty in machine learning models can be broken down into two parts: aleatoric (data-driven) and epistemic (model-driven). A new way to do this is presented, which gives us a better understanding of how certain our predictions are. This method works by looking at the data in a special way, using something called higher-order calibration. This allows us to estimate how uncertain we should be about our predictions without making any assumptions about the real-world data. It’s like having a map that shows us where we are and where we might go wrong. |
Keywords
» Artificial intelligence » Machine learning » Semantics
