Summary of Split Conformal Prediction Under Data Contamination, by Jase Clarkson et al.
Split Conformal Prediction under Data Contamination
by Jase Clarkson, Wenkai Xu, Mihai Cucuringu, Gesine Reinert
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers investigate the robustness of conformal prediction, a technique used to construct prediction intervals or sets from arbitrary models. They focus on split conformal prediction, which has low computational costs compared to model training. The study explores how data contamination affects the accuracy and efficiency of constructed sets when evaluated on clean test points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that predictions are accurate even if some of the data is wrong or misleading. It’s like trying to predict what will happen next in a game, but some of the moves you made earlier were fake. The researchers use special math to figure out how this might affect their predictions and propose ways to make them more reliable. |
