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David R. Burt
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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
1922019
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
1192020
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
742020
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
422020
Understanding variational inference in function-space
DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk
arXiv preprint arXiv:2011.09421, 2020
402020
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
282021
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
222019
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
192022
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
162021
Variational orthogonal features
DR Burt, CE Rasmussen, M van der Wilk
arXiv preprint arXiv:2006.13170, 2020
142020
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
102018
Crescent configurations
D Burt, E Goldstein, S Manski, SJ Miller, EA Palsson, H Suh
arXiv preprint arXiv:1509.07220, 2015
62015
Spectral Methods in Gaussian Process Approximations
DR Burt
Master’s thesis, University of Cambridge, 2018
52018
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
V Lalchand, W Bruinsma, D Burt, CE Rasmussen
Advances in Neural Information Processing Systems 35, 16612-16623, 2022
42022
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
42022
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
R Berlinghieri, BL Trippe, DR Burt, R Giordano, K Srinivasan, ...
arXiv preprint arXiv:2302.10364, 2023
32023
Numerically stable sparse gaussian processes via minimum separation using cover trees
A Terenin, DR Burt, A Artemev, S Flaxman, M van der Wilk, ...
Journal of Machine Learning Research 25, 1-36, 2024
22024
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
12022
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
SW Ober, DR Burt, A Artemev, M van der Wilk
NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and …, 2022
12022
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