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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 301–310 of 704 papers

TitleStatusHype
Semiparametric conformal prediction—0
Conformal-in-the-Loop for Learning with Imbalanced Noisy DataCode0
Strategic Conformal Prediction—0
Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework—0
Conformal Risk Minimization with Variance ReductionCode0
Uncertainty measurement for complex event prediction in safety-critical systems—0
Projected random forests and conformal prediction of circular dataCode0
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Conformal Prediction for Multimodal RegressionCode0
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