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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 511–520 of 704 papers

TitleStatusHype
Guaranteed Coverage Prediction Intervals with Gaussian Process Regression—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction—0
Conformal Contextual Robust Optimization—0
Estimating Uncertainty in Multimodal Foundation Models using Public Internet DataCode0
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions—0
Distribution-free risk assessment of regression-based machine learning algorithms—0
Conformal Predictions for Longitudinal Data—0
Confidence Calibration for Systems with Cascaded Predictive Modules—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
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