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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 581–590 of 704 papers

TitleStatusHype
Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model—0
Distribution-free Conformal Prediction for Ordinal Classification—0
Conformal calibrators—0
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators—0
Uncertainty measurement for complex event prediction in safety-critical systems—0
Conformalized Selective Regression—0
Robust Conformal Prediction with a Single Binary Certificate—0
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions—0
Conformalized Unconditional Quantile Regression—0
Conformalizing Machine Translation Evaluation—0
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