In this third talk of the series on high-arity learning frameworks, I will discuss the high-arity PAC learning theory, which is
motivated by PAC learning of graphs, hypergraphs and relational structures and is heavily inspired by (hyper)graph limits, and
is characterized by a slicewise notion of the Vapnik--Chervonenkis dimension.

I will also discuss how exchangeability theory
plays a crucial role in agnostic version of learning and a phenomenon exclusive to high-arity learning: the interplay between
the partite and non-partite. Time permitting, I will also talk about what part of the theory extends to learning hypergraph
limits.

No background in learning theory, model theory or hypergraph limits is required for this talk.

This talk is based on joint work with Maryanthe Malliaris.