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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 321–330 of 704 papers

TitleStatusHype
Adapting Conformal Prediction to Distribution Shifts Without Labels—0
Single Trajectory Conformal Prediction—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
Conformal Recursive Feature EliminationCode0
Valid Conformal Prediction for Dynamic GNNsCode0
Conformal Depression PredictionCode0
Verifiably Robust Conformal PredictionCode0
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal PredictionCode0
From Conformal Predictions to Confidence Regions—0
CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators—0
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