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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 641–650 of 704 papers

TitleStatusHype
Conformal Prediction in Dynamic Biological Systems—0
Conformal Prediction in Hierarchical Classification—0
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery—0
Safe Adaptive Cruise Control Under Perception Uncertainty: A Deep Ensemble and Conformal Tube Model Predictive Control Approach—0
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets—0
Conditional Conformal Risk Adaptation—0
Conformal Prediction Intervals for Markov Decision Process Trajectories—0
Conformal Prediction Intervals for Neural Networks Using Cross Validation—0
Concepts and Applications of Conformal Prediction in Computational Drug Discovery—0
Safe Merging in Mixed Traffic with Confidence—0
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