David R. Burt
David R. Burt
Verified email at cam.ac.uk - Homepage
Cited by
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Rates of convergence for sparse variational Gaussian process regression
D Burt, CE Rasmussen, M Van Der Wilk
International Conference on Machine Learning, 862-871, 2019
Convergence of Sparse Variational Inference in Gaussian Processes Regression
DR Burt, CE Rasmussen, M van der Wilk
Journal of Machine Learning Research 21 (131), 1-63, 2020
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
AYK Foong, DR Burt, Y Li, RE Turner
arXiv preprint arXiv:1909.00719, 2019
Pathologies of factorised Gaussian and MC dropout posteriors in Bayesian neural networks
AYK Foong, DR Burt, Y Li, RE Turner
Workshop on Bayesian Deep Learning, 2019
Bandit optimisation of functions in the Matérn kernel RKHS
D Janz, DR Burt, J González
arXiv preprint arXiv:2001.10396, 2020
Understanding Variational Inference in Function-Space
DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk
arXiv preprint arXiv:2011.09421, 2020
Variational Orthogonal Features
DR Burt, CE Rasmussen, M van der Wilk
arXiv preprint arXiv:2006.13170, 2020
Benford’s law and continuous dependent random variables
T Becker, D Burt, TC Corcoran, A Greaves-Tunnell, JR Iafrate, J Jing, ...
Annals of Physics 388, 350-381, 2018
Crescent configurations
D Burt, E Goldstein, S Manski, SJ Miller, EA Palsson, H Suh
arXiv preprint arXiv:1509.07220, 2015
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
A Artemev, DR Burt, M van der Wilk
arXiv preprint arXiv:2102.08314, 2021
Irrationality measure and lower bounds for π(x)
D Burt, S Donow, SJ Miller, M Schiffman, B Wieland
arXiv preprint arXiv:0709.2184, 2007
Spectral Methods in Gaussian Process Approximations
DR Burt
How Tight Can PAC-Bayes be in the Small Data Regime?
AYK Foong, WP Bruinsma, DR Burt, RE Turner
arXiv preprint arXiv:2106.03542, 2021
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