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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 561–570 of 704 papers

TitleStatusHype
Conformal Inductive Graph Neural Networks—0
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction—0
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series—0
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks—0
Retrain or not retrain: Conformal test martingales for change-point detection—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
Conformalized Answer Set Prediction for Knowledge Graph Embedding—0
Conformalized Credal Regions for Classification with Ambiguous Ground Truth—0
Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays—0
Conformalized Decision Risk Assessment—0
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