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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 261–270 of 704 papers

TitleStatusHype
Neurosymbolic Conformal Classification—0
A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in EchocardiographyCode0
Conformal Prediction in Dynamic Biological Systems—0
Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image ClassificationCode1
Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers—0
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
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