48 citations · 141 across the 14 of their papers we have counts for
3 papers · 1 filter
Barely Biased Learning for Gaussian Process Regression
David R. Burt, Artem Artemev, Mark van der Wilk
Recent work in scalable approximate Gaussian process regression has discussed a bias-variance-computation trade-off when estimating the log marginal likelihood. We suggest a method…
How Tight Can PAC-Bayes be in the Small Data Regime?
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt +1
In this paper, we investigate the question: Given a small number of datapoints, for example N = 30, how tight can PAC-Bayes and test set bounds be made? For such small datasets, te…
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
Artem Artemev, David R. Burt, Mark van der Wilk
We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix. We show t…