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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 311–320 of 704 papers

TitleStatusHype
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
Target Strangeness: A Novel Conformal Prediction Difficulty EstimatorCode0
SPARC: Prediction-Based Safe Control for Coupled Controllable and Uncontrollable Agents with Conformal Predictions—0
Building Conformal Prediction Intervals with Approximate Message PassingCode0
Conformal Predictive Portfolio Selection—0
Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict—0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information—0
Generative Conformal Prediction with Vectorized Non-Conformity Scores—0
Data-light Uncertainty Set Merging with Admissibility: Synthetics, Aggregation, and Test Inversion—0
Conjunction Subspaces Test for Conformal and Selective ClassificationCode0
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