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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 631–640 of 704 papers

TitleStatusHype
Fair Conformal Predictors for Applications in Medical ImagingCode0
Neural Predictive Monitoring under Partial ObservabilityCode0
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose—0
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty QuantificationCode1
MD-split+: Practical Local Conformal Inference in High Dimensions—0
Valid prediction intervals for regression problemsCode1
Can a single neuron learn predictive uncertainty?Code0
Locally Valid and Discriminative Prediction Intervals for Deep Learning ModelsCode1
Conformal Uncertainty Sets for Robust Optimization—0
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
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