Events

Date: 19 March 2026     

Location: Hybrid: MB503, SMS, QMUL, or via the Teams link (see seminar webpage)

The increasing availability of graph-structured data motivates a new type of optimisation problems over graph-based functions, i.e., searching for the graph or node that maximises the value of an underlying function. Such optimisation problems are challenging due to the search space that is discrete and high-dimensional, as well as the underlying function that is often black-box and expensive to evaluate. In this talk, I will provide several examples on how Bayesian optimisation can be used to optimise graph-based functions defined on graphs, node set of a graph, and node subsets of a graph. These are enabled by generalising Gaussian processes to graph-structured data, and they demonstrate the promise in combining probabilistic and geometric reasoning for analysing complex functions or solving machine learning tasks. Practical applications include automated machine learning, epidemiological source identification, and social influence maximisation.