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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 221–230 of 704 papers

TitleStatusHype
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Toward Conditional Distribution Calibration in Survival PredictionCode1
Conformal Prediction for Multimodal RegressionCode0
Target Strangeness: A Novel Conformal Prediction Difficulty EstimatorCode0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
Building Conformal Prediction Intervals with Approximate Message PassingCode0
SPARC: Prediction-Based Safe Control for Coupled Controllable and Uncontrollable Agents with Conformal Predictions—0
Conformal Predictive Portfolio Selection—0
Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict—0
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