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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 471–480 of 704 papers

TitleStatusHype
Conformalized Adaptive Forecasting of Heterogeneous TrajectoriesCode0
Conformal Predictive Programming for Chance Constrained Optimization—0
On Temperature Scaling and Conformal Prediction of Deep Classifiers—0
Discounted Adaptive Online Learning: Towards Better RegularizationCode0
Online conformal prediction with decaying step sizesCode0
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction—0
Conformal Prediction Sets Improve Human Decision MakingCode0
Generalization and Informativeness of Conformal Prediction—0
Cross-Validation Conformal Risk Control—0
Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection—0
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