Abstract
Predictions about the future are a crucial part of the decision making process in many real-world online problems. However, analysis of online algorithms has little to say about how to use predictions, and how properties of prediction errors impact algorithm design. In this talk, I'll describe recent results exploring the power of predictions in online convex optimization and how properties of prediction noise can impact the structure of optimal algorithms.