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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 481–490 of 704 papers

TitleStatusHype
Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction—0
On Temperature Scaling and Conformal Prediction of Deep Classifiers—0
One Sample is Enough to Make Conformal Prediction Robust—0
An Information Theoretic Perspective on Conformal Prediction—0
Trading Coverage for Precision: Conformal Prediction with Limited False Discoveries—0
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading—0
Online Multivalid Learning: Means, Moments, and Prediction Intervals—0
Online scalable Gaussian processes with conformal prediction for guaranteed coverage—0
Online Selective Conformal Prediction: Errors and Solutions—0
On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing—0
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