James Hensman

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Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Mark van der WilkAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
Keith WordenUniversity of SheffieldVerified email at sheffield.ac.uk
Alexander G. D. G. MatthewsDeepMindVerified email at google.com
Magnus RattrayUniversity of ManchesterVerified email at manchester.ac.uk
Nicolas DurrandePrincipal Scientist at MonumoVerified email at monumo.com
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Vincent DutordoirGoogle DeepMindVerified email at cam.ac.uk
ST JohnFinnish Center for Artificial Intelligence FCAI, Aalto UniversityVerified email at aalto.fi
Karen HolfordHonorary Distinguished Professor of Mechanical Engineering CardiffVerified email at cardiff.ac.uk
Mark J EatonCardiff UniversityVerified email at cf.ac.uk
Rhys PullinSenior Lecturer in Performance Engineering, Cardiff UniversityVerified email at cardiff.ac.uk
Artem ArtemevImperial College LondonVerified email at imperial.ac.uk
Stefanos EleftheriadisVerified email at amazon.com
Hugh SalimbeniImperial College LondonVerified email at ic.ac.uk
Gareth PierceProfessor Electrical and Electronic Engineering, University of StrathclydeVerified email at strath.ac.uk
Marc DeisenrothUniversity College LondonVerified email at ucl.ac.uk
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Vincent AdamPompeu Fabra UniversityVerified email at gatsby.ucl.ac.uk
Nicolò FusiPartner Researcher, Microsoft ResearchVerified email at microsoft.com

James Hensman

Microsoft Research

Verified email at microsoft.com

machine learning probabilistic modelling biostatistics Gaussian processes approximate inference


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Gaussian processes for big data J Hensman, N Fusi, ND Lawrence Conference on Uncertainty in Artificial Intellegence, 282--290, 2013	1562	2013
Scalable Variational Gaussian Process Classification J Hensman, A Matthews, Z Ghahramani AISTATS 38, 2014	764	2014
GPflow: A Gaussian process library using TensorFlow AGG Matthews, M Van Der Wilk, T Nickson, K Fujii, A Boukouvalas, P Le, ... Journal of Machine Learning Research 18 (40), 1-6, 2017	657	2017
Natural computing for mechanical systems research: A tutorial overview K Worden, WJ Staszewski, JJ Hensman Mechanical Systems and Signal Processing 25 (1), 4-111, 2011	343	2011
Variational Fourier features for Gaussian processes J Hensman, N Durrande, A Solin Journal of Machine Learning Research 18 (151), 1-52, 2018	245	2018
On sparse variational methods and the Kullback-Leibler divergence between stochastic processes AGG Matthews, J Hensman, R Turner, Z Ghahramani AISTATS 51, 231-239, 2016	199*	2016
Locating acoustic emission sources in complex structures using Gaussian processes J Hensman, R Mills, SG Pierce, K Worden, M Eaton Mechanical Systems and Signal Processing 24 (1), 211-223, 2010	191	2010
GPy: A gaussian process framework in python J Hensman, N Fusi, R Andrade, N Durrande, A Saul, M Zwiessele, ...	177*	2012
MCMC for variationally sparse Gaussian processes J Hensman, AGG Matthews, M Filippone, Z Ghahramani NIPS 2015, 2015	171	2015
Convolutional Gaussian processes M van der Wilk, CE Rasmussen, J Hensman NeurIPS 2017, 2017	170	2017
The circadian clock in murine chondrocytes regulates genes controlling key aspects of cartilage homeostasis N Gossan, L Zeef, J Hensman, A Hughes, JF Bateman, L Rowley, ... Arthritis & Rheumatism 65 (9), 2334-2345, 2013	151	2013
Fast variational inference in the conjugate exponential family J Hensman, M Rattray, ND Lawrence NIPS 2012, 2012	138	2012
Identification of Gaussian process state space models S Eleftheriadis, T Nicholson, M Deisenroth, J Hensman Advances in neural information processing systems 30, 2017	133	2017
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters J Hensman, ND Lawrence, M Rattray BMC bioinformatics 14, 1-12, 2013	128	2013
Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference K Worden, JJ Hensman Mechanical Systems and Signal Processing 32, 153-169, 2012	126	2012
A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview KM Holford, MJ Eaton, JJ Hensman, R Pullin, SL Evans, N Dervilis, ... Progress in Aerospace Sciences 90, 1-11, 2017	115	2017
A framework for interdomain and multioutput Gaussian processes M Van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman arXiv preprint arXiv:2003.01115, 2020	110	2020
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models H Salimbeni, S Eleftheriadis, J Hensman AISTATS, 2018	109	2018
Hoxa2 selectively enhances Meis binding to change a branchial arch ground state S Amin, IJ Donaldson, DA Zannino, J Hensman, M Rattray, M Losa, ... Developmental cell 32 (3), 265-277, 2015	95	2015
Learning Invariances using the Marginal Likelihood M van der Wilk, M Bauer, ST John, J Hensman NeurIPS 2018, 2018	92	2018

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