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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 231–240 of 704 papers

TitleStatusHype
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction—0
Flow-based Conformal Prediction for Multi-dimensional Time Series—0
Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts—0
Robust Conformal Outlier Detection under Contaminated Reference DataCode0
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market—0
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems—0
Calibrated Physics-Informed Uncertainty Quantification—0
Conformal Uncertainty Indicator for Continual Test-Time Adaptation—0
Multivariate Conformal Prediction using Optimal Transport—0
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