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Mark van der Wilk
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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
475*2017
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
260*2017
Understanding probabilistic sparse Gaussian process approximations
M Bauer, M van der Wilk, CE Rasmussen
Advances in neural information processing systems 29, 2016
2202016
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
1762014
Convolutional Gaussian Processes
M van der Wilk, CE Rasmussen, J Hensman
Advances in Neural Information Processing Systems, 2845-2854, 2017
1192017
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
1162019
Bayesian layers: A module for neural network uncertainty
D Tran, M Dusenberry, M van der Wilk, D Hafner
Advances in neural information processing systems 32, 2019
892019
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
552020
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
502018
Bayesian neural network priors revisited
V Fortuin, A Garriga-Alonso, F Wenzel, G Rńtsch, R Turner, ...
International Conference on Learning Representations (ICLR), 2022
452022
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
40*2020
Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty
M Monteiro, LL Folgoc, DC de Castro, N Pawlowski, B Marques, ...
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
392020
The promises and pitfalls of deep kernel learning
SW Ober, CE Rasmussen, M van der Wilk
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificialá…, 2021
332021
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 (AISTATSá…, 2020
30*2020
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
272020
Understanding variational inference in function-space
DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk
arXiv preprint arXiv:2011.09421, 2020
252020
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
24*2019
Sparse Gaussian process approximations and applications
M van der Wilk
University of Cambridge, 2019
192019
Speedy Performance Estimation for Neural Architecture Search
R Ru, C Lyle, L Schut, M Fil, M van der Wilk, Y Gal
Advances in Neural Information Processing Systems 34, 4079-4092, 2021
18*2021
A bayesian perspective on training speed and model selection
C Lyle, L Schut, R Ru, Y Gal, M van der Wilk
Advances in Neural Information Processing Systems 33, 10396-10408, 2020
152020
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