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Anastasios and Stephen present a gentle introduction to conformal prediction and distribution-free uncertainty quantification. The tutorial focuses on generating confidence intervals for any model and data set with minimal computational cost. The key concept is obtaining not only point predictions from a neural network but also prediction intervals with high probability. The techniques discussed operate in a black box manner, eliminating the need for model analysis or retraining.
