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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 511–520 of 704 papers

TitleStatusHype
Adaptive Conformal Regression with Jackknife+ Rescaled Scores—0
Conformal Prediction with Large Language Models for Multi-Choice Question AnsweringCode1
Federated Conformal Predictors for Distributed Uncertainty QuantificationCode1
Uncertainty Quantification over Graph with Conformalized Graph Neural NetworksCode1
Distribution-Free Matrix Prediction Under Arbitrary Missing Pattern—0
Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees—0
Conformal Nucleus Sampling—0
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling—0
Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision—0
Conformal Regression in Calorie Prediction for Team Jumbo-Visma—0
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