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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 251–260 of 704 papers

TitleStatusHype
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction—0
Conformal Regression in Calorie Prediction for Team Jumbo-Visma—0
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms—0
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators—0
Calibrating AI Models for Wireless Communications via Conformal Prediction—0
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose—0
Distribution-free Conformal Prediction for Ordinal Classification—0
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction—0
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction—0
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