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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 431–440 of 704 papers

TitleStatusHype
Theoretical Foundations of Conformal Prediction—0
The Penalized Inverse Probability Measure for Conformal Classification—0
Are foundation models for computer vision good conformal predictors?—0
Learning Pareto-Efficient Decisions with Confidence—0
Learning Robust Decision Policies from Observational Data—0
Learning Temporal Logic Predicates from Data with Statistical Guarantees—0
Valid Error Bars for Neural Weather Models using Conformal Prediction—0
The Relationship between No-Regret Learning and Online Conformal Prediction—0
Valid Selection among Conformal Sets—0
Three Applications of Conformal Prediction for Rating Breast Density in Mammography—0
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