Department Colloquium

A central challenge in modern machine learning is learning generalizable procedures that remain effective on unseen, potentially out-of-distribution (OOD) data. Such generalization depends on a complex interplay among model architectures, task structures, data assumptions, and training methodologies. In this talk, I will focus on the interaction between model architecture and task structure in the context of graph learning. We are particularly interested in two questions: Do different graph neural networks learn fundamentally different algorithmic procedures? And can OOD generalization be achieved with only finite samples? How do we probe what's learned internally? To explore these questions, I will present our initial studies using two concrete settings, graph partitioning/clustering and graph shortest-path computation, as testbeds for understanding how graph models internalize and apply algorithmic structure. This talk is based on joint work with several collaborators, whom I will acknowledge during the talk.