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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 291–300 of 704 papers

TitleStatusHype
Conformal calibrators—0
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
Conformal Predictions under Markovian Data—0
Conformal Predictions for Longitudinal Data—0
Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence—0
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