From Distributed Algorithms to Machine Learning and Back

Roger Wattenhofer

Abstract: In the realm of computer science, it may seem that distributed computing and machine learning exist on opposite ends of the spectrum. However, there are many connections between the two domains, both in theory and practice. Recently, machine learning research has become excited about graphs. And when machine learning meets graphs, researchers familiar with distributed algorithms may experience a sense of déjà vu, as many classic distributed computing paradigms are being rediscovered. It feels a bit like "machine learning + graphs = distributed algorithms." In my talk, I am going to introduce some key concepts in graph machine learning such as underreaching and oversquashing. These concepts have been known in the distributed computing community as local and congest, respectively. In the main part of the talk, I am going to present some recent breakthroughs in this exciting intersection of fields. Finally, I will also present some intriguing open problems.