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Anastasios and Stephen provide a gentle introduction to conformal prediction and distribution-free uncertainty quantification. They explain how to generate rigorous confidence intervals that work for any model and data set, with minimal computational cost. By using neural networks, they can provide not only point predictions but also prediction intervals with high probability. The key feature of their techniques is that they work in a black box manner, eliminating the need for model analysis or retraining.
