Summary of Generalization and Informativeness Of Conformal Prediction, by Matteo Zecchin et al.
Generalization and Informativeness of Conformal Prediction
by Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Fredrik Hellström
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 how to ensure machine learning modules in decision-making processes provide accurate and informative predictions. A key technique is conformal prediction (CP), which transforms a base predictor into a set predictor with guaranteed coverage. While CP provides control over the predicted set’s coverage, it lacks control over the average size of the sets, which affects their informativeness. The authors establish a theoretical connection between the base predictor’s generalization properties and the CP prediction set’s informativeness. They derive an upper bound on the expected size of the CP set predictor, dependent on calibration data, target reliability, and base predictor performance. This work is significant for machine learning applications where uncertainty quantification is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make predictions in decision-making processes more accurate and helpful. One way to do this is by using a technique called conformal prediction (CP). CP helps ensure that the predicted range of possibilities includes the true answer, but it doesn’t control how much information is provided in each prediction. The authors study how the performance of the base predictor affects the informativeness of the CP predictions. They find a way to calculate an upper limit on the amount of information provided by CP and show that this depends on the quality of the data used for calibration, how reliable we want the predictions to be, and how well the base predictor performs. |
Keywords
* Artificial intelligence * Generalization * Machine learning
