Statistics seminar

Abstract: This talk reviews recent advances in machine learning for combinatorial optimization, with a particular focus on routing problems such as the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP).

First, I will present a unifying high-level hierarchy of methods. I will then delve deeper into end-to-end reinforcement learning approaches, which have shown strong empirical performance. Finally, I will present our recent work on multi-objective routing over multigraphs, highlighting how learning-based models can handle competing objectives and complex network structures.