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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 351–360 of 704 papers

TitleStatusHype
Formal Verification and Control with Conformal Prediction—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Can Transformers Do Enumerative Geometry?Code0
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification—0
Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction—0
Conformalized Interval Arithmetic with Symmetric CalibrationCode0
Adaptive Uncertainty Quantification for Generative AI—0
Conformalized Answer Set Prediction for Knowledge Graph Embedding—0
A conformalized learning of a prediction set with applications to medical imaging classification—0
Safety-Critical Control with Offline-Online Neural Network InferenceCode0
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