Abstract: Valid uncertainty quantification is crucial for high-stakes decision-making. Conformal inference provides a powerful framework that can wrap around any black-box prediction algorithm, like random forests or deep neural networks, and generate prediction intervals with distribution-free coverage guarantees. In this talk, I will describe how conformal inference can be adapted to handle...