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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 341–350 of 704 papers

TitleStatusHype
Entropy Reweighted Conformal Classification—0
Conformal Nucleus Sampling—0
Conformal Uncertainty Sets for Robust Optimization—0
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction—0
Conformal Off-Policy Evaluation in Markov Decision Processes—0
A conformalized learning of a prediction set with applications to medical imaging classification—0
Conformal Uncertainty Indicator for Continual Test-Time Adaptation—0
Causal Responder Detection—0
Conformal Off-Policy Prediction for Multi-Agent Systems—0
Conformal Inductive Graph Neural Networks—0
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