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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 621–630 of 704 papers

TitleStatusHype
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market—0
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Uncertainty Quantification for Neurosymbolic Programs via Compositional Conformal Prediction—0
Conformal Prediction for Hierarchical Data—0
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification—0
Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry—0
A conformalized learning of a prediction set with applications to medical imaging classification—0
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