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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 341–350 of 704 papers

TitleStatusHype
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in GraphsCode0
Adjusting Regression Models for Conditional Uncertainty CalibrationCode0
Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors—0
Neurosymbolic Conformal Classification—0
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes—0
A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs—0
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in EchocardiographyCode0
Conformal Prediction in Dynamic Biological Systems—0
Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers—0
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