Viacheslav Borovitskiy: Geometric Gaussian Processes


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 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.


Viacheslav Borovitskiy is a researcher interested in mathematically rich problems in machine learning. His works in the area received paper awards at the ICML & AISTATS conferences.

Viacheslav obtained his PhD in the field of mathematics (harmonic analysis) from St. Petersburg Department of Steklov Mathematical Institute (PDMI RAS) in 2022.

Having received the ETH Zürich Postdoctoral Fellowship, he is now a postdoc at the Learning & Adaptive Systems Group of ETH Zürich led by Prof. Andreas Krause.

Marc Deisenroth
Marc Deisenroth
DeepMind Chair of Machine Learning and Artificial Intelligence