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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 121–130 of 704 papers

TitleStatusHype
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification—0
Conformal coronary calcification volume estimation with conditional coverage via histogram clustering—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction—0
Probabilistic Conformal Prediction with Approximate Conditional Validity—0
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions—0
Conditional Conformal Risk Adaptation—0
Are foundation models for computer vision good conformal predictors?—0
Adversarially Robust Conformal Prediction—0
A Cross-Conformal Predictor for Multi-label Classification—0
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