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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 161–170 of 704 papers

TitleStatusHype
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects—0
Anomalous Edge Detection in Edge Exchangeable Social Network Models—0
A conformalized learning of a prediction set with applications to medical imaging classification—0
Conformal Loss-Controlling Prediction—0
Adaptive Conformal Regression with Jackknife+ Rescaled Scores—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey—0
Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees—0
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces—0
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