\dm_csml_event_details UCL ELLIS

Non-Euclidean Matérn Gaussian Processes


Speaker

Alexander Terenin

Affiliation

Cambridge University

Date

Friday, 06 May 2022

Time

12:00-13:00

Location

Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH

Link

https://ucl.zoom.us/j/97245943682

Event series

Jump Trading/ELLIS CSML Seminar Series

Abstract

In recent years, the machine learning community has become increasingly interested in learning in settings where data lives in non-Euclidean spaces, for instance in applications to physics and engineering, or other settings where it is important that symmetries are enforced. In this talk, we will develop a class of Gaussian process models defined on Riemannian manifolds and graphs, and show how to effectively perform all computations needed to train these models using standard automatic-differentiation-based methods. This gives an effective framework to deploy data-efficient interactive decision-making systems such as Bayesian optimization to settings with symmetries and invariances.

Biography

Alexander Terenin is a Postdoctoral Research Associate at the University of Cambridge. He is interested in statistical machine learning, particularly in settings where the data is not fixed, but is gathered interactively by the learning machine. This leads naturally to Gaussian processes and data-efficient interactive decision-making systems such as Bayesian optimization, to areas such as multi-armed bandits and reinforcement learning, and to techniques for incorporating inductive biases and prior information such as symmetries into machine learning models.