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).
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).
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.
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).