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All publications by Neil D. Lawrence
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Fast Variational Inference in the Conjugate Exponential Family
James Hensman, Magnus Rattray and Neil D. Lawrence
Advances in Neural Information Processing Systems 25, 2012


Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models
Alfredo Kalaitzis and Neil D. Lawrence
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Manifold Relevance Determination
Andreas Damianou, Carl Ek, Michalis K. Titsias and Neil D. Lawrence
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Variational Gaussian Process Dynamical Systems
Andreas Damianou, Michalis K. Titsias and Neil D. Lawrence
Advances in Neural Information Processing Systems 24, 2011


Efficient inference in matrix-variate Gaussian models with \iid observation noise
Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence and Karsten M. Borgwardt
Advances in Neural Information Processing Systems 24, 2011


Spectral Dimensionality Reduction via Maximum Entropy
Neil D. Lawrence
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Switched Latent Force Models for Movement Segmentation
Mauricio Alvarez, Jan R. Peters, Neil D. Lawrence and Bernhard Schölkopf
Advances in Neural Information Processing Systems 23, 2010


Bayesian Gaussian Process Latent Variable Model
Michalis K. Titsias and Neil D. Lawrence
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
Mauricio A. Álvarez, David Luengo, Michalis K. Titsias and Neil D. Lawrence
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Non-linear matrix factorization with Gaussian processes
Neil D. Lawrence and Raquel Urtasun
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Latent Force Models
Mauricio A. Álvarez, David Luengo and Neil D. Lawrence
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Topologically-constrained latent variable models
Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell and Neil D. Lawrence
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Efficient Sampling for Gaussian Process Inference using Control Variables
Neil D. Lawrence, Magnus Rattray and Michalis K. Titsias
Advances in Neural Information Processing Systems 21, 2008


Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
Ben Calderhead, Mark Girolami and Neil D. Lawrence
Advances in Neural Information Processing Systems 21, 2008


Sparse Convolved Gaussian Processes for Multi-output Regression
Mauricio Alvarez and Neil D. Lawrence
Advances in Neural Information Processing Systems 21, 2008


Hierarchical Gaussian process latent variable models
Neil D. Lawrence and Andrew J. Moore
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Neil D. Lawrence
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Local distance preservation in the GP-LVM through back constraints
Neil D. Lawrence and Joaquin Q. Candela
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Modelling transcriptional regulation using Gaussian Processes
Neil D. Lawrence, Guido Sanguinetti and Magnus Rattray
Advances in Neural Information Processing Systems 19, 2006


Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
Tonatiuh P. Centeno and Neil D. Lawrence
Journal of Machine Learning Research, 2006


Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
Neil D. Lawrence
Journal of Machine Learning Research, 2005


Learning to learn with the informative vector machine
Neil D. Lawrence and John C. Platt
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Semi-supervised Learning via Gaussian Processes
Neil D. Lawrence and Michael I. Jordan
Advances in Neural Information Processing Systems 17, 2004


Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
Neil D. Lawrence
Advances in Neural Information Processing Systems 16, 2003


Fast Sparse Gaussian Process Methods: The Informative Vector Machine
Ralf Herbrich, Neil D. Lawrence and Matthias Seeger
Advances in Neural Information Processing Systems 15, 2002