Summary of Utility-directed Conformal Prediction: a Decision-aware Framework For Actionable Uncertainty Quantification, by Santiago Cortes-gomez et al.
Utility-Directed Conformal Prediction: A Decision-Aware Framework for Actionable Uncertainty Quantification
by Santiago Cortes-Gomez, Carlos Patiño, Yewon Byun, Steven Wu, Eric Horvitz, Bryan Wilder
First submitted to arxiv on: 2 Oct 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 The paper proposes a novel approach to uncertainty quantification in machine learning models by incorporating downstream decisions into conformal prediction. This method, which accounts for the cost of incorrect predictions, is designed to improve performance on subsequent decision problems. By harnessing the strengths of conformal methods, including modularity, model-agnosticism, and statistical coverage guarantees, the approach achieves significantly lower costs than standard conformal methods in empirical evaluations across various datasets and utility metrics. The method also retains standard coverage guarantees, making it suitable for high-stakes decision-making applications. A real-world use case in healthcare diagnosis demonstrates the effectiveness of the approach in generating sets with coherent diagnostic meaning, which can aid triage decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make machine learning models better at making decisions. Right now, we don’t consider how our predictions might affect future decisions. But what if we could train our models to think about the consequences of their choices? The authors propose a method that does just that by using something called conformal prediction. This approach takes into account the cost of getting things wrong and tries to make better predictions as a result. It’s like training a doctor to make more accurate diagnoses by thinking about what might happen if they misdiagnose someone. The paper shows that this approach works well in practice, especially when it comes to making high-stakes decisions. |
Keywords
» Artificial intelligence » Machine learning
