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Sparse Gaussian Processes with Spherical Harmonic Features


Jun 30, 2020
Vincent Dutordoir, Nicolas Durrande, James Hensman

* International Conference on Machine, PMLR 119, 2020 

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Amortized variance reduction for doubly stochastic objectives


Mar 09, 2020
Ayman Boustati, Sattar Vakili, James Hensman, ST John


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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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Doubly Sparse Variational Gaussian Processes


Jan 15, 2020
Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman

* Accepted at AISTATS 2020 

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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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Deep Gaussian Processes with Importance-Weighted Variational Inference


May 14, 2019
Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth

* Appearing ICML 2019 

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Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era


Feb 26, 2019
Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman

* Proceedings of the 22 nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan. PMLR: Volume 89 

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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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Infinite-Horizon Gaussian Processes


Nov 15, 2018
Arno Solin, James Hensman, Richard E. Turner

* To appear in Advances in Neural Information Processing Systems (NIPS 2018) 

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Gaussian Process Conditional Density Estimation


Oct 30, 2018
Vincent Dutordoir, Hugh Salimbeni, Marc Deisenroth, James Hensman


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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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Large-Scale Cox Process Inference using Variational Fourier Features


Apr 03, 2018
S. T. John, James Hensman


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Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models


Mar 24, 2018
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman


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Variational Fourier features for Gaussian processes


Nov 08, 2017
James Hensman, Nicolas Durrande, Arno Solin


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Identification of Gaussian Process State Space Models


Nov 07, 2017
Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc Peter Deisenroth, 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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Pseudo-extended Markov chain Monte Carlo


Aug 17, 2017
Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman


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Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction


Aug 16, 2017
Hossein Soleimani, James Hensman, Suchi Saria

* To appear in IEEE Transaction on Pattern Analysis and Machine Intelligence 

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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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Chained Gaussian Processes


Apr 18, 2016
Alan D. Saul, James Hensman, Aki Vehtari, Neil D. Lawrence

* Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain 

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On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes


Dec 04, 2015
Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani

* 9 pages. No figures 

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MCMC for Variationally Sparse Gaussian Processes


Jun 12, 2015
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani

* 16 pages 

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Spike and Slab Gaussian Process Latent Variable Models


May 10, 2015
Zhenwen Dai, James Hensman, Neil Lawrence


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Nested Variational Compression in Deep Gaussian Processes


Dec 03, 2014
James Hensman, Neil D. Lawrence


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Scalable Variational Gaussian Process Classification


Nov 07, 2014
James Hensman, Alex Matthews, Zoubin Ghahramani

* 16 pages, 9 figures 

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Gaussian Process Models with Parallelization and GPU acceleration


Oct 18, 2014
Zhenwen Dai, Andreas Damianou, James Hensman, Neil Lawrence


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Fast nonparametric clustering of structured time-series


Apr 14, 2014
James Hensman, Magnus Rattray, Neil D. Lawrence

* Accepted for publication in special edition of TPAMI on Bayesian Nonparametrics 

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Gaussian Processes for Big Data


Sep 26, 2013
James Hensman, Nicolo Fusi, Neil D. Lawrence

* Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013) 

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Fast Variational Inference in the Conjugate Exponential Family


Dec 04, 2012
James Hensman, Magnus Rattray, Neil D. Lawrence

* Accepted at NIPS 2012 

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