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Found 4 papers, 1 papers with code
Efficient Conformal Prediction under Data Heterogeneity
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions.
Selective Nonparametric Regression via Testing
Prediction with the possibility of abstention (or selective prediction) is an important problem for error-critical machine learning applications.
Scalable computation of prediction intervals for neural networks via matrix sketching
Accounting for the uncertainty in the predictions of modern neural networks is a challenging and important task in many domains.
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions.
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