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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 521–530 of 704 papers

TitleStatusHype
Conformal Temporal Logic Planning using Large Language Models—0
Data-driven Reachability using Christoffel Functions and Conformal Prediction—0
Closing the Loop on Runtime Monitors with Fallback-Safe MPC—0
Conditional validity of heteroskedastic conformal regressionCode0
Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data miningCode0
Adaptive conformal classification with noisy labelsCode0
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification—0
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence—0
An Uncertainty-Aware Pseudo-Label Selection Framework using Regularized Conformal PredictionCode0
Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification—0
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