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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 461–470 of 704 papers

TitleStatusHype
Robust Conformal Prediction with a Single Binary Certificate—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee—0
Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering—0
Robust Online Conformal Prediction under Uniform Label Noise—0
A conformalized learning of a prediction set with applications to medical imaging classification—0
Conformalized Link Prediction on Graph Neural Networks—0
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
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