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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 601–610 of 704 papers

TitleStatusHype
Training Uncertainty-Aware Classifiers with Conformalized Deep LearningCode1
On the Utility of Prediction Sets in Human-AI Teams—0
Towards PAC Multi-Object Detection and TrackingCode1
On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing—0
Raman Spectrum Matching with Contrastive Representation Learning—0
Efficient and Differentiable Conformal Prediction with General Function ClassesCode0
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
Data-SUITE: Data-centric identification of in-distribution incongruous examplesCode0
Adaptive Conformal Predictions for Time SeriesCode1
Conformal Prediction Sets with Limited False PositivesCode0
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