Robust and Conjugate Gaussian Process Regression
Matias Altamirano François-Xavier Briol Jeremias Knoblauch
To enable closed form conditioning, a common assumption in Gaussian process
(GP) regression is independent and identically distributed Gaussian observation
noise. This strong and simplistic assumption is often violated in practice,
which leads to unreliable inferences and uncertainty quantification.
Unfortunately, existing methods for robustifying GPs break closed-form
conditioning, which makes them less attractive to practitioners and
significantly more computationally expensive. In this paper, we demonstrate how
to perform provably robust and conjugate Gaussian process (RCGP) regression at
virtually no additional cost using generalised Bayesian inference. RCGP is
particularly versatile as it enables exact conjugate closed form updates in all
settings where standard GPs admit them. To demonstrate its strong empirical
performance, we deploy RCGP for problems ranging from Bayesian optimisation to
sparse variational Gaussian processes.