Gaussian processes (GPs) are often considered to be the gold standard in settings where well-calibrated predictive uncertainty is of utter importance, such as decision making.
It is important for applications to have a class of general purpose GPs. Traditionally, these are the stationary processes, e.g. RBF or Matrn GPs, at least for the usual vectorial inputs. For non-vectorial inputs, however, there is often no such class. This state of affairs hinders the use of GPs in a number of application areas ranging from robotics to drug design.
In this talk, I will consider GPs taking inputs on a manifold, on a node set of a graph, or in a discrete space of graphs. I will discuss a framework for defining the appropriate general purpose GPs, as well as the analytic and numerical techniques that make them tractable.