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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 311–320 of 704 papers

TitleStatusHype
CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition—0
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series—0
Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework—0
Disturbance-Adaptive Data-Driven Predictive Control: Trading Comfort Violations for Savings in Building Climate Control—0
Disturbance-adaptive Model Predictive Control for Bounded Average Constraint Violations—0
CONSeg: Voxelwise Glioma Conformal Segmentation—0
Domain Adaptive Skin Lesion Classification via Conformal Ensemble of Vision Transformers—0
Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting—0
Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records—0
Bridging conformal prediction and scenario optimization—0
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