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The Promises and Pitfalls of Deep Kernel Learning


Feb 24, 2021
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk

* 18 pages 

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Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients


Feb 16, 2021
Artem Artemev, David R. Burt, Mark van der Wilk

* Preprint 

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Bayesian Neural Network Priors Revisited


Feb 12, 2021
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard Turner, Mark van der Wilk, Laurence Aitchison


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Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks


Jan 11, 2021
Adrià Garriga-Alonso, Mark van der Wilk

* Presented at 3rd Symposium on Advances in Approximate Bayesian Inference 

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Design of Experiments for Verifying Biomolecular Networks


Nov 25, 2020
Ruby Sedgwick, John Goertz, Molly Stevens, Ruth Misener, Mark van der Wilk

* Comment: Updated to correct typo "that that" => "that" 

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Understanding Variational Inference in Function-Space


Nov 18, 2020
David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk

* 19 pages 

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A Bayesian Perspective on Training Speed and Model Selection


Oct 27, 2020
Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk

* To be presented at NeurIPS 2020 

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Convergence of Sparse Variational Inference in Gaussian Processes Regression


Aug 01, 2020
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

* Journal of Machine Learning Research, 21(131), 1-63 (2020) 
* Extended version of http://proceedings.mlr.press/v97/burt19a.html (arxiv version: arXiv:1903.03571 ). Published in Journal of Machine Learning Research: http://jmlr.org/papers/v21/19-1015.html. Code available at: https://github.com/markvdw/RobustGP 

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Variational Orthogonal Features


Jun 23, 2020
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk


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Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty


Jun 10, 2020
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker

* 17 pages, 11 figures, 2 tables 

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Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search


Jun 08, 2020
Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal

* 14 pages, 10 figures 

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On the Benefits of Invariance in Neural Networks


May 01, 2020
Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy


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Capsule Networks -- A Probabilistic Perspective


Apr 07, 2020
Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk


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A Framework for Interdomain and Multioutput Gaussian Processes


Mar 02, 2020
Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, James Hensman


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Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes


Jun 22, 2019
Creighton Heaukulani, Mark van der Wilk


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Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models


Jun 13, 2019
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

* PMLR 97:2931-2940 (2019) 
* 10 pages, 4 figures, 3 tables. Published in the proceedings of the Thirty-sixth International Conference on Machine Learning (ICML), 2019 

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Rates of Convergence for Sparse Variational Gaussian Process Regression


Mar 08, 2019
David R. Burt, Carl E. Rasmussen, Mark van der Wilk


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Translation Insensitivity for Deep Convolutional Gaussian Processes


Feb 15, 2019
Vincent Dutordoir, Mark van der Wilk, Artem Artemev, Marcin Tomczak, James Hensman


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Non-Factorised Variational Inference in Dynamical Systems


Dec 14, 2018
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

* 6 pages, 1 figure, 1 table 

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Bayesian Layers: A Module for Neural Network Uncertainty


Dec 11, 2018
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner

* Presented in NeurIPS 2018 workshop Bayesian Deep Learning. Code available at https://github.com/tensorflow/tensor2tensor 

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Closed-form Inference and Prediction in Gaussian Process State-Space Models


Dec 10, 2018
Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen

* 7 pages, 6 figures 

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Learning Invariances using the Marginal Likelihood


Aug 16, 2018
Mark van der Wilk, Matthias Bauer, ST John, James Hensman


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Convolutional Gaussian Processes


Sep 06, 2017
Mark van der Wilk, Carl Edward Rasmussen, James Hensman

* To appear in Advances in Neural Information Processing Systems 30 (NIPS 2017) 

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Understanding Probabilistic Sparse Gaussian Process Approximations


May 30, 2017
Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen

* published in Advances in Neural Information Processing Systems 29 (NIPS 2016) 

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GPflow: A Gaussian process library using TensorFlow


Oct 27, 2016
Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, James Hensman


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Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial


Sep 29, 2014
Yarin Gal, Mark van der Wilk

* 20 pages, no figures 

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Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models


Sep 29, 2014
Yarin Gal, Mark van der Wilk, Carl E. Rasmussen

* 9 pages, 8 figures 

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