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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 631–640 of 704 papers

TitleStatusHype
Conformal Prediction for Natural Language Processing: A Survey—0
Conformal Prediction for Network-Assisted Regression—0
Conformal Prediction for STL Runtime Verification—0
Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets—0
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification—0
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
Conformal Prediction for Trustworthy Detection of Railway Signals—0
Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction—0
Probabilistic Conformal Prediction with Approximate Conditional Validity—0
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