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Found 3 papers, 3 papers with code
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability.
Conformal inference is (almost) free for neural networks trained with early stopping
Early stopping based on hold-out data is a popular regularization technique designed to mitigate overfitting and increase the predictive accuracy of neural networks.
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities.
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