Summary of Calibrated Uncertainty Quantification For Operator Learning Via Conformal Prediction, by Ziqi Ma et al.
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction
by Ziqi Ma, Kamyar Azizzadenesheli, Anima Anandkumar
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a novel approach to operator learning, specifically designed for scientific and engineering applications where calibrated uncertainty quantification is crucial. The authors introduce a risk-controlling quantile neural operator, a distribution-free method that can handle complex datasets without making strong assumptions like Gaussianity. The proposed approach provides a theoretical calibration guarantee on the coverage rate, which measures the expected percentage of points whose true value lies within the predicted uncertainty ball. Experimental results on two tasks – 2D Darcy flow and 3D car surface pressure prediction – validate the method’s effectiveness in providing calibrated coverage and efficient uncertainty bands, outperforming baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to make predictions more accurate by using a special kind of machine learning called operator learning. The problem is that this type of learning usually doesn’t tell us exactly how good its predictions are going to be. The authors came up with a new way to do this, which they call the risk-controlling quantile neural operator. This method is really good at figuring out when its predictions are likely to be right or wrong. They tested it on two real-world problems and showed that it works much better than other methods. |
Keywords
* Artificial intelligence * Machine learning
