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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 141–150 of 704 papers

TitleStatusHype
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
Max-Rank: Efficient Multiple Testing for Conformal Prediction—0
Conformal Prediction: A Data Perspective—0
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
Conformal Prediction as Bayesian Quadrature—0
Conformal Prediction for Hierarchical Data—0
Closing the Loop on Runtime Monitors with Fallback-Safe MPC—0
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators—0
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