Publications

Sebastian Popescu, Ben Glocker, Mark van der Wilk (2022). Matrix Inversion free variational inference in Conditional Student's T Processes. Fourth Symposium on Advances in Approximate Bayesian Inference.

Mark van der Wilk, Artem Artemev, James Hensman (2022). Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes. Fourth Symposium on Advances in Approximate Bayesian Inference.

Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk (2022). Last Layer Marginal Likelihood for Invariance Learning. Proceedings of the Twenty Fifth International Conference on Artificial Intelligence and Statistics (AISTATS).

Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard Turner, Mark van der Wilk, Laurence Aitchison (2022). Bayesian Neural Network Priors Revisited. The Tenth International Conference on Learning Representations (ICLR).

Artem Artemev, David R. Burt, Mark van der Wilk (2021). Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. Proceedings of the 38th International Conference on Machine Learning (ICML).

Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk (2021). The Promises and Pitfalls of Deep Kernel Learning. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI).

Adrià Garriga-Alonso, Mark van der Wilk (2021). Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI).

David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk (2021). Understanding Variational Inference in Function-Space. Third Symposium on Advances in Approximate Bayesian Inference.

Binxin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal (2021). Speedy Performance Estimation for Neural Architecture Search. Advances in Neural Information Processing Systems (NeurIPS).

Tycho van der Ouderaa, Mark van der Wilk (2021). Learning Invariant Weights in Neural Networks. ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning.

Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc P. Deisenroth, ST John (2021). GPflux: A Library for Deep Gaussian Processes.

Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande (2021). Deep Neural Networks as Point Estimates for Deep Gaussian Processes. Advances in Neural Information Processing Systems (NeurIPS).

Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison (2021). Data augmentation in Bayesian neural networks and the cold posterior effect.

David R. Burt, Artem Artemev, Mark van der Wilk (2021). Barely Biased Learning for Gaussian Process Regression.

Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman (2020). Bayesian Image Classification with Deep Convolutional Gaussian Processes. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS).

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk (2020). Variational Orthogonal Features.

Mark van der Wilk, ST John, Artem Artemev, James Hensman (2020). Variational Gaussian Process Models without Matrix Inverses. Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference.

Miguel Monteiro, Loic Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker (2020). Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. Advances in Neural Information Processing Systems (NeurIPS).

Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy (2020). On the Benefits of Invariance in Neural Networks.

Ruby Sedgwick, John Goertz, Molly Stevens, Ruth Misener, Mark van der Wilk (2020). Design of Experiments for Verifying Biomolecular Networks.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk (2020). Convergence of Sparse Variational Inference in Gaussian Processes Regression. Journal of Machine Learning Research.

Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk (2020). Capsule Networks -- A Probabilistic Perspective.

Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, James Hensman (2020). A Framework for Interdomain and Multioutput Gaussian Processes.

Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk (2020). A Bayesian Perspective on Training Speed and Model Selection. Advances in Neural Information Processing Systems (NeurIPS).

David Burt, Carl Edward Rasmussen, Mark van der Wilk (2019). Rates of Convergence for Sparse Variational Gaussian Process Regression. Proceedings of the 36th International Conference on Machine Learning (ICML).

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen (2019). Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. Proceedings of the 36th International Conference on Machine Learning (ICML).

Mark van der Wilk (2019). Sparse Gaussian process approximations and applications.

Creighton Heaukulani, Mark van der Wilk (2019). Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. Advances in Neural Information Processing Systems 32 (NeurIPS).

Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner (2019). Bayesian Layers: A Module for Neural Network Uncertainty. Advances in Neural Information Processing Systems 32 (NeurIPS).

Mark van der Wilk, Matthias Bauer, ST John, James Hensman (2018). Learning Invariances using the Marginal Likelihood. Advances in Neural Information Processing Systems 31 (NeurIPS).

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman (2017). GPflow: A Gaussian Process Library using TensorFlow. Journal of Machine Learning Research.

Mark van der Wilk, Carl Edward Rasmussen, James Hensman (2017). Convolutional Gaussian Processes. Advances in Neural Information Processing Systems 30 (NIPS).

Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Vivian Weller (2017). Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning.

Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen (2017). Closed-form Inference and Prediction in Gaussian Process State-Space Models. NIPS 2017 Workshop on Time Series.

Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen (2016). Understanding Probabilistic Sparse Gaussian Process Approximations. Advances in Neural Information Processing Systems 29 (NIPS).

Rowan McAllister, Mark van der Wilk, Carl Edward Rasmussen (2016). Data-Efficient Policy Search using PILCO and Directed-Exploration. ICML 2016 Workshop on Data-Efficient Machine Learning.

Yarin Gal, Mark van der Wilk (2014). Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial.

Mark van der Wilk, Andrew Gordon Wilson, Carl Edward Rasmussen (2014). Variational Inference for Latent Variable Modelling of Correlation Structure. NIPS 2014 Workshop of Advances in Variational Inference.

Yarin Gal, Mark van der Wilk, Carl Edward Rasmussen (2014). Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. Advances in Neural Information Processing Systems 27 (NIPS).