Calibration, Decisions, and Collaboration in Learning

In this tutorial we will learn about a powerful framework to make probabilistic predictions in ways that "look like real probabilities" in all of the ways that matter for downstream applications. We'll see how to do this efficiently even in difficult, adversarial environments, and then focus on two concrete applications.
First we'll see how to make predictions that are "trustworthy" for downstream decision makers. Many downstream decision makers, each with different objectives and actions, will be able to act optimally as if our predictions are correct, and get strong guarantees about their performance. Next, we'll see how to make predictions that allow for efficient collaboration between two differently informed parties, like an AI and a human user, who can't easily share their observations, while still obtaining the complementary benefits of their individual knowledge. We'll end with a quick survey of many other applications of this technique.