Neil D. Lawrence
Neil D. Lawrence
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Probabilistic non-linear principal component analysis with Gaussian process latent variable models.
N Lawrence
Journal of machine learning research 6 (11), 2005
Dataset shift in machine learning
J Quiñonero-Candela, M Sugiyama, ND Lawrence, A Schwaighofer
Mit Press, 2009
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
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
Kernels for vector-valued functions: A review
MA Alvarez, L Rosasco, ND Lawrence
arXiv preprint arXiv:1106.6251, 2011
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
Sparse Convolved Gaussian Processes for Multi-output Regression.
MA Alvarez, ND Lawrence
NIPS 21, 57-64, 2008
Batch Bayesian optimization via local penalization
J González, Z Dai, P Hennig, N Lawrence
Artificial intelligence and statistics, 648-657, 2016
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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