Margaux Zaffran and Aymeric Dieuleveut gave an overview of techniques (including split conformal prediction, full conformal prediction, and Jackknife+) for doing predictive uncertainty quantification with minimal assumptions (requiring exchangeable, but not necessarily independent and identically distributed, data).
In addition to the theoretical foundations, they also covered two case studies — regression on noisy medical images and time-series regression on energy prices — to show how these techniques could be applied.
The slides for the tutorial can be found here (large PDF, can take a while to load).