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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 301–310 of 704 papers

TitleStatusHype
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Conformal Prediction Regions are Imprecise Highest Density Regions—0
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
Label Noise Robustness of Conformal Prediction—0
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling—0
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning—0
A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions—0
Adapting Conformal Prediction to Distribution Shifts Without Labels—0
Confidence Calibration for Systems with Cascaded Predictive Modules—0
Conformal Prediction Intervals for Neural Networks Using Cross Validation—0
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