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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 91–100 of 704 papers

TitleStatusHype
Locally Valid and Discriminative Prediction Intervals for Deep Learning ModelsCode1
Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty PropagationCode1
Class-Conditional Conformal Prediction with Many ClassesCode1
Multi-class probabilistic classification using inductive and cross Venn-Abers predictorsCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction—0
A Conformal Predictive Measure for Assessing Catastrophic Forgetting—0
Conformal Contextual Robust Optimization—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Adapting Conformal Prediction to Distribution Shifts Without Labels—0
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