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Found 3 papers, 3 papers with code
Probabilistic Calibration by Design for Neural Network Regression
To address the miscalibration issue of neural networks, various methods have been proposed to improve calibration, including post-hoc methods that adjust predictions after training and regularization methods that act during training.
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
A primary objective is to generate a distribution-free joint prediction region for the arrival time and mark, with a finite-sample marginal coverage guarantee.
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
We also analyze the performance of recalibration, conformal, and regularization methods to enhance probabilistic calibration.
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