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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 451–460 of 704 papers

TitleStatusHype
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
Selecting informative conformal prediction sets with false coverage rate control—0
COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic CircuitsCode0
PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare—0
CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control—0
Safe Merging in Mixed Traffic with Confidence—0
End-to-end Conditional Robust Optimization—0
Confidence on the Focal: Conformal Prediction with Selection-Conditional CoverageCode0
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access—0
Distribution-Free Guarantees for Systems with Decision-Dependent Noise—0
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