Abstract

Gaussian processes (GPs) are often considered to be the gold standard in settings where well-calibrated predictive uncertainty is of key 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 Matérn 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.

Link to join virtually: https://cam-ac-uk.zoom.us/j/89473073451

A recording of this talk is available at the following link: https://www.cl.cam.ac.uk/seminars/wednesday/video/

This talk is being recorded. If you do not wish to be seen in the recording, please avoid sitting in the front six rows of the central section in the lecture theatre. Any questions asked will also be included in the recording. The recording will be made available on the Department’s webpage