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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 391–400 of 704 papers

TitleStatusHype
The Penalized Inverse Probability Measure for Conformal Classification—0
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions—0
Conformal Prediction for Class-wise Coverage via Augmented Label Rank CalibrationCode0
Robust Conformal Prediction Using Privileged InformationCode0
Normalizing Flows for Conformal RegressionCode0
Learning Cellular Network Connection Quality with Conformal—0
Certifiably Byzantine-Robust Federated Conformal PredictionCode0
Adapting Conformal Prediction to Distribution Shifts Without Labels—0
Single Trajectory Conformal Prediction—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
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