New Conformal Prediction method tackles noisy labels in regression

Researchers have developed a new method for Conformal Prediction (CP) that effectively handles regression models trained with noisy labels. This approach establishes a mathematically sound procedure to estimate the correct CP threshold, even when the calibration data contains inaccuracies. The proposed algorithm is designed to be practical for continuous regression problems and has demonstrated superior performance compared to existing methods on medical imaging datasets with simulated label noise, achieving results close to those obtained with clean data. AI

IMPACT Improves reliability of AI predictions in critical applications like medical imaging by accounting for imperfect training data.

RANK_REASON Academic paper detailing a new method for conformal prediction. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• Conformal Prediction
• DagsHub
• Gaussian function
• Hugging Face
• IArxiv
• Yahav Cohen

• paper
• safety

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