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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 411–420 of 704 papers

TitleStatusHype
Online conformal prediction with decaying step sizesCode0
Non-Exchangeable Conformal Language Generation with Nearest NeighborsCode1
Conformal Prediction Sets Improve Human Decision MakingCode0
Cross-Validation Conformal Risk Control—0
Generalization and Informativeness of Conformal Prediction—0
Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling—0
Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection—0
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Uncertainty quantification for probabilistic machine learning in earth observation using conformal predictionCode1
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point ProcessesCode0
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