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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 151–160 of 704 papers

TitleStatusHype
Validation of Conformal Prediction in Cervical Atypia Classification—0
Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting ModelCode0
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events—0
Real-Time Privacy Preservation for Robot Visual Perception—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Fair Uncertainty Quantification for Depression Prediction—0
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction—0
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weightingCode0
Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees—0
Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures—0
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