| GPflow: A Gaussian process library using TensorFlow AGG Matthews, M van der Wilk, T Nickson, K Fujii, A Boukouvalas, ... Journal of Machine Learning Research 18 (1), 1299-1304, 2017 | 310* | 2017 |
| Distributed variational inference in sparse Gaussian process regression and latent variable models Y Gal*, M van der Wilk*, CE Rasmussen Advances in Neural Information Processing Systems, 3257-3265, 2014 | 156 | 2014 |
| Understanding probabilistic sparse Gaussian process approximations M Bauer, M van der Wilk, CE Rasmussen arXiv preprint arXiv:1606.04820, 2016 | 155 | 2016 |
| Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning R McAllister, Y Gal, A Kendall, M van der Wilk, A Shah, R Cipolla, ... International Joint Conferences on Artificial Intelligence, Inc., 2017 | 145* | 2017 |
| Convolutional Gaussian Processes M van der Wilk, CE Rasmussen, J Hensman Advances in Neural Information Processing Systems, 2845-2854, 2017 | 77 | 2017 |
| Rates of Convergence for Sparse Variational Gaussian Process Regression DR Burt, CE Rasmussen, M van der Wilk Proceedings of the 36th International Conference on Machine Learning (ICML 2019), 2019 | 67 | 2019 |
| Bayesian layers: A module for neural network uncertainty D Tran, MW Dusenberry, M van der Wilk, D Hafner arXiv preprint arXiv:1812.03973, 2018 | 36 | 2018 |
| Learning invariances using the marginal likelihood M van der Wilk, M Bauer, ST John, J Hensman Advances in Neural Information Processing Systems 31, 9938-9948, 2018 | 18 | 2018 |
| Bayesian Image Classification with Deep Convolutional Gaussian Processes V Dutordoir, M van der Wilk, A Artemev, J Hensman International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020 | 15* | 2020 |
| Overcoming mean-field approximations in recurrent Gaussian process models AD Ialongo, M Van Der Wilk, J Hensman, CE Rasmussen Proceedings of the 36th International Conference on Machine Learning (ICML 2019), 2019 | 11* | 2019 |
| A framework for interdomain and multioutput Gaussian processes M van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman arXiv preprint arXiv:2003.01115, 2020 | 9 | 2020 |
| Closed-form Inference and Prediction in Gaussian Process State-Space Models AD Ialongo, M van der Wilk, CE Rasmussen NIPS 2017 Time-Series Workshop, 2017 | 8 | 2017 |
| On the benefits of invariance in neural networks C Lyle, M van der Wilk, M Kwiatkowska, Y Gal, B Bloem-Reddy arXiv preprint arXiv:2005.00178, 2020 | 6 | 2020 |
| Sparse Gaussian process approximations and applications M van der Wilk University of Cambridge, 2019 | 6 | 2019 |
| A practical guide to Gaussian processes MP Deisenroth, Y Luo, MVD Wilk | 5 | 2019 |
| Variational inference in sparse Gaussian process regression and latent variable models-a gentle tutorial Y Gal, M van der Wilk arXiv preprint arXiv:1402.1412, 2014 | 5 | 2014 |
| Convergence of Sparse Variational Inference in Gaussian Processes Regression DR Burt, CE Rasmussen, M van der Wilk Journal of Machine Learning Research 21, 1-63, 2020 | 4 | 2020 |
| Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty M Monteiro, LL Folgoc, DC de Castro, N Pawlowski, B Marques, ... arXiv preprint arXiv:2006.06015, 2020 | 3 | 2020 |
| Revisiting the train loss: an efficient performance estimator for neural architecture search B Ru, C Lyle, L Schut, M van der Wilk, Y Gal arXiv preprint arXiv:2006.04492, 2020 | 3 | 2020 |
| Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes C Heaukulani, M van der Wilk Advances in Neural Information Processing Systems 32 (NIPS 2019), 2019 | 3 | 2019 |
James HensmanPrincipal scientist, AmazonVerified email at amazon.com
