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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 451–460 of 704 papers

TitleStatusHype
Adaptive Temperature Scaling with Conformal Prediction—0
Max-Rank: Efficient Multiple Testing for Conformal Prediction—0
Mirror Online Conformal Prediction with Intermittent Feedback—0
Mitigating LLM Hallucinations via Conformal Abstention—0
ACHO: Adaptive Conformal Hyperparameter Optimization—0
Model-Agnostic Uncertainty Quantification for Fast NFC Tag Identification using RF Fingerprinting—0
Model-free generalized fiducial inference—0
Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection—0
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs—0
Multi-Agent Reachability Calibration with Conformal Prediction—0
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