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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 361–370 of 704 papers

TitleStatusHype
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal Prediction to High-Dimensional Covariate Shifts—0
Making learning more transparent using conformalized performance prediction—0
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering—0
MD-split+: Practical Local Conformal Inference in High Dimensions—0
Meta-Analysis with Untrusted Data—0
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction—0
Adversarially Robust Conformal Prediction—0
Mirror Online Conformal Prediction with Intermittent Feedback—0
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