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This tutorial provides a gentle introduction to conformal prediction and distribution-free uncertainty quantification, focusing on generating rigorous confidence intervals that work for any model and data set with low computational cost. The approach involves feeding inputs into a neural network to obtain point predictions and confidence intervals for the desired output with high probability. The key features of the techniques discussed include working in a black box manner without the need for model analysis or retraining.
