Summary of Length Optimization in Conformal Prediction, by Shayan Kiyani et al.
Length Optimization in Conformal Prediction
by Shayan Kiyani, George Pappas, Hamed Hassani
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME)
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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 Conditional validity and length efficiency are two essential aspects of conformal prediction (CP). A novel framework, Conformal Prediction with Length-Optimization (CPL), reconciles these objectives by constructing prediction sets with optimal length while ensuring conditional validity under various covariate shifts. The paper provides strong duality results for CPL in the infinite sample regime and shows conditionally valid prediction sets in the finite sample regime. Empirical evaluations demonstrate superior performance of CPL on diverse real-world and synthetic datasets across classification, regression, and large language model-based multiple choice question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CP helps predict outcomes with uncertainty quantification. A new method, Conformal Prediction with Length-Optimization (CPL), makes this more efficient while keeping predictions accurate for specific groups. This works well even when there are changes in the data. The researchers showed that CPL is good and better than other methods on many types of datasets. |
Keywords
* Artificial intelligence * Classification * Large language model * Optimization * Question answering * Regression
