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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 111–120 of 704 papers

TitleStatusHype
Robust Conformal Prediction with a Single Binary Certificate—0
Conformal Prediction with Upper and Lower Bound Models—0
Conformal forecasting for surgical instrument trajectory—0
Conformal prediction of future insurance claims in the regression problem—0
Improving the statistical efficiency of cross-conformal predictionCode0
CONSeg: Voxelwise Glioma Conformal Segmentation—0
State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems—0
Universality of conformal prediction under the assumption of randomness—0
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
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