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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 551–560 of 704 papers

TitleStatusHype
Conformal Counterfactual Inference under Hidden Confounding—0
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout—0
Reliable Prediction Intervals with Regression Neural Networks—0
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions—0
Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
Conformal forecasting for surgical instrument trajectory—0
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering—0
Conformal Group Recommender System—0
conformalClassification: A Conformal Prediction R Package for Classification—0
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