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David R. Burt
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Year
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
1392019
On the expressiveness of approximate inference in Bayesian neural networks
A Foong, D Burt, Y Li, R Turner
Advances in Neural Information Processing Systems 33, 15897-15908, 2020
692020
Convergence of sparse variational inference in Gaussian processes regression
DR Burt, CE Rasmussen, M Van Der Wilk
The Journal of Machine Learning Research 21 (1), 5120-5182, 2020
392020
Understanding variational inference in function-space
DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk
arXiv preprint arXiv:2011.09421, 2020
282020
Bandit optimisation of functions in the Matérn kernel RKHS
D Janz, D Burt, J González
International Conference on Artificial Intelligence and Statistics, 2486-2495, 2020
262020
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
172019
Variational orthogonal features
DR Burt, CE Rasmussen, M van der Wilk
arXiv preprint arXiv:2006.13170, 2020
102020
How Tight Can PAC-Bayes be in the Small Data Regime?
A Foong, W Bruinsma, D Burt, R Turner
Advances in Neural Information Processing Systems 34, 4093-4105, 2021
92021
Tighter bounds on the log marginal likelihood of Gaussian process regression using conjugate gradients
A Artemev, DR Burt, M Van Der Wilk
International Conference on Machine Learning, 362-372, 2021
82021
Wide Mean-Field Bayesian Neural Networks Ignore the Data
B Coker, WP Bruinsma, DR Burt, W Pan, F Doshi-Velez
International Conference on Artificial Intelligence and Statistics, 5276-5333, 2022
62022
Crescent configurations
D Burt, E Goldstein, S Manski, SJ Miller, EA Palsson, H Suh
arXiv preprint arXiv:1509.07220, 2015
62015
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
52018
Spectral Methods in Gaussian Process Approximations
DR Burt
Master’s thesis, University of Cambridge, 2018
42018
A Note on the Chernoff Bound for Random Variables in the Unit Interval
AYK Foong, WP Bruinsma, DR Burt
arXiv preprint arXiv:2205.07880, 2022
22022
Barely Biased Learning for Gaussian Process Regression
DR Burt, A Artemev, M van der Wilk
arXiv preprint arXiv:2109.09417, 2021
22021
Scalable Approximate Inference and Model Selection in Gaussian Process Regression
D Burt
University of Cambridge, 2022
12022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
V Lalchand, WP Bruinsma, DR Burt, CE Rasmussen
arXiv preprint arXiv:2211.02476, 2022
2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
A Terenin, DR Burt, A Artemev, S Flaxman, M van der Wilk, ...
arXiv preprint arXiv:2210.07893, 2022
2022
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
SW Ober, DR Burt, A Artemev, M van der Wilk
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Articles 1–19