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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
WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts—0
Individualised Counterfactual Examples Using Conformal Prediction Intervals—0
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures—0
Validity, consonant plausibility measures, and conformal prediction—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
JANET: Joint Adaptive predictioN-region Estimation for Time-series—0
JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows—0
Joint Registration and Conformal Prediction for Partially Observed Functional Data—0
KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection—0
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