Summary of Conformalized Selective Regression, by Anna Sokol et al.
Conformalized Selective Regression
by Anna Sokol, Nuno Moniz, Nitesh Chawla
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposes a novel approach to selective regression, allowing prediction models to abstain from predictions in cases of considerable uncertainty. The traditional focus on distribution-based proxies for measuring uncertainty has neglected the influence of model-specific biases on performance. The proposed method, conformalized selective regression, uses conformal prediction to provide grounded confidence measures based on model-specific biases. This approach demonstrates an advantage over state-of-the-art baselines through extensive experimentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way for computers to make predictions. Sometimes these predictions can be wrong or uncertain. Instead of always trying to predict something, this method lets the computer decide not to predict if it’s really unsure. This helps by making sure the predictions are accurate and fair. The old way of doing this relied too much on how likely a prediction was, but this new approach takes into account how well the computer is doing in general. By using this method, computers can make better predictions and we can trust them more. |
Keywords
* Artificial intelligence * Regression
