| 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 | 187 | 2019 |
| 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 | 116 | 2020 |
| 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 | 72 | 2020 |
| 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 | 41 | 2020 |
| Understanding variational inference in function-space DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk arXiv preprint arXiv:2011.09421, 2020 | 40 | 2020 |
| 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 | 27 | 2021 |
| 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 | 22 | 2019 |
| 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 | 19 | 2022 |
| 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 | 16 | 2021 |
| Variational orthogonal features DR Burt, CE Rasmussen, M van der Wilk arXiv preprint arXiv:2006.13170, 2020 | 14 | 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 | 10 | 2018 |
| Crescent configurations D Burt, E Goldstein, S Manski, SJ Miller, EA Palsson, H Suh arXiv preprint arXiv:1509.07220, 2015 | 6 | 2015 |
| Spectral Methods in Gaussian Process Approximations DR Burt Master’s thesis, University of Cambridge, 2018 | 5 | 2018 |
| 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 | 4 | 2022 |
| 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 | 3 | 2022 |
| 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 | 2 | 2024 |
| 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 | 2 | 2023 |
| Barely Biased Learning for Gaussian Process Regression DR Burt, A Artemev, M van der Wilk arXiv preprint arXiv:2109.09417, 2021 | 2 | 2021 |
| Scalable Approximate Inference and Model Selection in Gaussian Process Regression D Burt | 1 | 2022 |
| 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 | 1 | 2022 |
Mark van der WilkAssociate Professor, University of OxfordПотвърден имейл адрес: cs.ox.ac.uk
