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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 21–30 of 704 papers

TitleStatusHype
Conformal coronary calcification volume estimation with conditional coverage via histogram clustering—0
Conformal Prediction for Zero-Shot ModelsCode1
Conformal Object Detection by Sequential Risk Control—0
JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows—0
Individualised Counterfactual Examples Using Conformal Prediction Intervals—0
Test-time augmentation improves efficiency in conformal prediction—0
Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints—0
Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification—0
Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays—0
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score—0
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