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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 111–120 of 704 papers

TitleStatusHype
Assumption-free fidelity bounds for hardware noise characterization—0
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling—0
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction—0
Probabilistic Conformal Prediction with Approximate Conditional Validity—0
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