  Machine learning methods are increasingly widely used in high-risk settings
such as healthcare, transportation, and finance. In these settings, it is
important that a model produces calibrated uncertainty to reflect its own
confidence and avoid failures. In this paper we survey recent works on
uncertainty quantification (UQ) for deep learning, in particular
distribution-free Conformal Prediction method for its mathematical properties
and wide applicability. We will cover the theoretical guarantees of conformal
methods, introduce techniques that improve calibration and efficiency for UQ in
the context of spatiotemporal data, and discuss the role of UQ in the context
of safe decision making.
