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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 121–130 of 704 papers

TitleStatusHype
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage—0
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Volume Optimality in Conformal Prediction with Structured Prediction Sets—0
Rectifying Conformity Scores for Better Conditional Coverage—0
Joint Registration and Conformal Prediction for Partially Observed Functional Data—0
Sparse Activations as Conformal PredictorsCode0
Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global PerturbationsCode0
Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal Prediction to High-Dimensional Covariate Shifts—0
Conformal Prediction as Bayesian Quadrature—0
The Relationship between No-Regret Learning and Online Conformal Prediction—0
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