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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 411–420 of 704 papers

TitleStatusHype
Generative Conformal Prediction with Vectorized Non-Conformity Scores—0
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction—0
PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare—0
A Conformal Approach to Feature-based Newsvendor under Model Misspecification—0
Partial-Label Learning with Conformal Candidate Cleaning—0
PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification—0
Physics Constrained Motion Prediction with Uncertainty Quantification—0
Posterior Conformal Prediction—0
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging—0
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