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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 181–190 of 704 papers

TitleStatusHype
Conformal Prediction: A Data Perspective—0
Conformal Prediction and Human Decision Making—0
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
Conformal Prediction as Bayesian Quadrature—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
Conformal Prediction Bands for Two-Dimensional Functional Time Series—0
Conformalized Unconditional Quantile Regression—0
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions—0
Closing the Loop on Runtime Monitors with Fallback-Safe MPC—0
Online Calibrated and Conformal Prediction Improves Bayesian Optimization—0
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