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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 381–390 of 704 papers

TitleStatusHype
Gaussian process interpolation with conformal prediction: methods and comparative analysis—0
Generalization and Informativeness of Conformal Prediction—0
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction—0
GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Graph-Structured Feedback Multimodel Ensemble Online Conformal Prediction—0
Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification—0
Group conditional validity via multi-group learning—0
Guaranteed Coverage Prediction Intervals with Gaussian Process Regression—0
Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals—0
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