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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 601–610 of 704 papers

TitleStatusHype
Label Noise Robustness of Conformal Prediction—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey—0
Conformal Prediction Bands for Two-Dimensional Functional Time Series—0
A novel Deep Learning approach for one-step Conformal Prediction approximationCode0
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction—0
Improved conformalized quantile regression—0
ACHO: Adaptive Conformal Hyperparameter Optimization—0
"Even if ..." -- Diverse Semifactual Explanations of RejectCode0
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction SetsCode0
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