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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 71–80 of 704 papers

TitleStatusHype
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor MappingCode0
SConU: Selective Conformal Uncertainty in Large Language Models—0
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios—0
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer—0
Leave-One-Out Stable Conformal PredictionCode0
Conditional Conformal Risk Adaptation—0
FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair—0
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging—0
Conformal Robust Beamforming via Generative Channel Models—0
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