Mark van der Wilk
TitleCited byYear
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
201*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
1312014
Understanding probabilistic sparse Gaussian process approximations
M Bauer, M van der Wilk, CE Rasmussen
Advances in neural information processing systems, 1533-1541, 2016
922016
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
642017
Convolutional Gaussian Processes
M van der Wilk, CE Rasmussen, J Hensman
Advances in Neural Information Processing Systems, 2845-2854, 2017
402017
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
182019
Bayesian layers: A module for neural network uncertainty
D Tran, M Dusenberry, M van der Wilk, D Hafner
Advances in Neural Information Processing Systems, 14633-14645, 2019
82019
Variational inference in sparse Gaussian process regression and latent variable models–a gentle tutorial
Y Gal, M van der Wilk
arXiv preprint arXiv 1402, 2014
6*2014
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
42017
Translation insensitivity for deep convolutional Gaussian processes
V Dutordoir, M van der Wilk, A Artemev, M Tomczak, J Hensman
arXiv preprint arXiv:1902.05888, 2019
32019
Non-Factorised Variational Inference in Dynamical Systems
A Davide Ialongo, M van der Wilk, J Hensman, CE Rasmussen
arXiv preprint arXiv:1812.06067, 2018
32018
Sparse Gaussian process approximations and applications
M van der Wilk
University of Cambridge, 2019
22019
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
12019
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
12018
Data-Efficient Policy Search using PILCO and Directed-Exploration
R McAllister, M van der Wilk, CE Rasmussen
ICML 2016 Workshop on Data-Efficient Machine Learning, 2016
1*2016
Variational Inference for Latent Variable Modelling of Correlation Structure
M van der Wilk, AG Wilson, CE Rasmussen
Advances in Variational Inference Workshop (NIPS 2014), 2014
12014
A practical guide to gaussian processes
MP Deisenroth, Y Luo, M van der Wilk
1
Variational Gaussian Process Models without Matrix Inverses
M van der Wilk, ST John, A Artemev, J Hensman
Symposium on Advances in Approximate Bayesian Inference, 1-9, 2020
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
2019
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Articles 1–19