Summary of Validation Of Ml-uq Calibration Statistics Using Simulated Reference Values: a Sensitivity Analysis, by Pascal Pernot
Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis
by Pascal Pernot
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
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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 explores the limitations of popular machine learning uncertainty quantification (ML-UQ) calibration statistics, which often lack predefined reference values. This makes it challenging to validate calibration and relies on reader interpretation. To address this issue, synthetic calibrated datasets are used to simulate reference values. However, the generative probability distribution for simulating errors can impact the sensitivity of these reference values, raising concerns about calibration diagnostics like CC (correlation coefficient between absolute errors and uncertainties) and ENCE (expected normalized calibration error). The study highlights the excessive sensitivity of certain statistics to unknown generative distributions and proposes a robust validation workflow. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how some ways we measure how well our machine learning models are calibrated don’t have clear standards, making it hard to know if they’re good or not. They want to fix this by using fake data to create standard values. But then they realize that the way they generate these fake errors can affect the results, which makes it tricky for certain measures like correlation coefficient and expected normalized calibration error. The researchers are trying to find a better way to check if our models are well-calibrated. |
Keywords
* Artificial intelligence * Machine learning * Probability
