Neil D. Lawrence
Neil D. Lawrence
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Cited by
Cited by
Dataset shift in machine learning
J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
Mit Press, 2008
Probabilistic non-linear principal component analysis with Gaussian process latent variable models.
N Lawrence
Journal of machine learning research 6 (11), 2005
Gaussian process latent variable models for visualisation of high dimensional data.
ND Lawrence
Nips 2, 5, 2003
Gaussian processes for big data
J Hensman, N Fusi, ND Lawrence
arXiv preprint arXiv:1309.6835, 2013
Deep Gaussian processes
A Damianou, ND Lawrence
Artificial intelligence and statistics, 207-215, 2013
Wifi-slam using gaussian process latent variable models.
B Ferris, D Fox, ND Lawrence
IJCAI 7 (1), 2480-2485, 2007
Kernels for vector-valued functions: A review
MA Alvarez, L Rosasco, ND Lawrence
arXiv preprint arXiv:1106.6251, 2011
Fast sparse Gaussian process methods: The informative vector machine
N Lawrence, M Seeger, R Herbrich
Proceedings of the 16th annual conference on neural information processing …, 2003
Fast forward selection to speed up sparse Gaussian process regression
MW Seeger, CKI Williams, ND Lawrence
International Workshop on Artificial Intelligence and Statistics, 254-261, 2003
Bayesian Gaussian process latent variable model
M Titsias, ND Lawrence
Proceedings of the Thirteenth International Conference on Artificial …, 2010
Advances in Neural Information Processing Systems
Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger
Inc.: Red Hook, NY, USA 27, 2014
Learning to learn with the informative vector machine
ND Lawrence, JC Platt
Proceedings of the twenty-first international conference on Machine learning, 65, 2004
Computationally efficient convolved multiple output Gaussian processes
MA Alvarez, ND Lawrence
The Journal of Machine Learning Research 12, 1459-1500, 2011
Non-linear matrix factorization with Gaussian processes
ND Lawrence, R Urtasun
Proceedings of the 26th annual international conference on machine learning …, 2009
Local distance preservation in the GP-LVM through back constraints
ND Lawrence, J Quinonero-Candela
Proceedings of the 23rd international conference on Machine learning, 513-520, 2006
Batch Bayesian optimization via local penalization
J González, Z Dai, P Hennig, N Lawrence
Artificial intelligence and statistics, 648-657, 2016
Sparse Convolved Gaussian Processes for Multi-output Regression.
MA Alvarez, ND Lawrence
NIPS 21, 57-64, 2008
Estimating a kernel fisher discriminant in the presence of label noise
N Lawrence, B Schölkopf
18th International Conference on Machine Learning (ICML 2001), 306-306, 2001
Hierarchical Gaussian process latent variable models
ND Lawrence, AJ Moore
Proceedings of the 24th international conference on Machine learning, 481-488, 2007
Semi-supervised learning via Gaussian processes
N Lawrence, M Jordan
Advances in neural information processing systems 17, 753-760, 2004
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