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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 481–490 of 704 papers

TitleStatusHype
Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling—0
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point ProcessesCode0
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction—0
SymmPI: Predictive Inference for Data with Group SymmetriesCode0
Efficient Conformal Prediction under Data Heterogeneity—0
Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions—0
Faithful Model Explanations through Energy-Constrained Conformal CounterfactualsCode0
Android Malware Detection with Unbiased Confidence Guarantees—0
Reliable Prediction Intervals with Regression Neural Networks—0
Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction—0
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