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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 501–510 of 704 papers

TitleStatusHype
On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling—0
On the Expected Size of Conformal Prediction SetsCode0
Conformalizing Machine Translation Evaluation—0
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift—0
On training locally adaptive CPCode0
A Large-Scale Study of Probabilistic Calibration in Neural Network RegressionCode1
Conformal Prediction with Missing ValuesCode1
Enterprise Disk Drive Scrubbing Based on Mondrian Conformal Predictors—0
Conformal Prediction with Partially Labeled DataCode0
Quantifying Deep Learning Model Uncertainty in Conformal Prediction—0
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