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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 611–620 of 704 papers

TitleStatusHype
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
Conformal Prediction as Bayesian Quadrature—0
Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering—0
Conformal Prediction Bands for Two-Dimensional Functional Time Series—0
Conformal Prediction based Spectral Clustering—0
Robust Online Conformal Prediction under Uniform Label Noise—0
Adversarially Robust Conformal Prediction—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Conformal Prediction for Distribution-free Optimal Control of Linear Stochastic Systems—0
Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction—0
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