Neil D. Lawrence Vis

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41EENeil D. Lawrence, Raquel Urtasun: Non-linear matrix factorization with Gaussian processes. ICML 2009: 76
40EERichard D. Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence, Magnus Rattray: puma: a Bioconductor package for propagating uncertainty in microarray analysis. BMC Bioinformatics 10: (2009)
39EEPei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence: Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. ECCB 2008: 70-75
38EERaquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence: Topologically-constrained latent variable models. ICML 2008: 1080-1087
37EECarl Henrik Ek, Jonathan Rihan, Philip H. S. Torr, Grégory Rogez, Neil D. Lawrence: Ambiguity Modeling in Latent Spaces. MLMI 2008: 62-73
36EEMichalis Titsias, Neil D. Lawrence, Magnus Rattray: Efficient Sampling for Gaussian Process Inference using Control Variables. NIPS 2008: 1681-1688
35EEBen Calderhead, Mark Girolami, Neil D. Lawrence: Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. NIPS 2008: 217-224
34EEMauricio Alvarez, Neil D. Lawrence: Sparse Convolved Gaussian Processes for Multi-output Regression. NIPS 2008: 57-64
33EELuka Eciolaza, M. Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming: Gaussian Process Latent Variable Models for Fault Detection. CIDM 2007: 287-292
32EENeil D. Lawrence, Andrew J. Moore: Hierarchical Gaussian process latent variable models. ICML 2007: 481-488
31EEBrian Ferris, Dieter Fox, Neil D. Lawrence: WiFi-SLAM Using Gaussian Process Latent Variable Models. IJCAI 2007: 2480-2485
30EECarl Henrik Ek, Philip H. S. Torr, Neil D. Lawrence: Gaussian Process Latent Variable Models for Human Pose Estimation. MLMI 2007: 132-143
29EERaquel Urtasun, David J. Fleet, Neil D. Lawrence: Modeling Human Locomotion with Topologically Constrained Latent Variable Models. Workshop on Human Motion 2007: 104-118
28EEGuido Sanguinetti, Magnus Rattray, Neil D. Lawrence: Identifying Submodules of Cellular Regulatory Networks. CMSB 2006: 155-168
27EENathaniel J. King, Neil D. Lawrence: Fast Variational Inference for Gaussian Process Models Through KL-Correction. ECML 2006: 270-281
26EEGuido Sanguinetti, Neil D. Lawrence: Missing Data in Kernel PCA. ECML 2006: 751-758
25EENeil D. Lawrence, Joaquin Quiñonero Candela: Local distance preservation in the GP-LVM through back constraints. ICML 2006: 513-520
24EENeil D. Lawrence, Guido Sanguinetti, Magnus Rattray: Modelling transcriptional regulation using Gaussian Processes. NIPS 2006: 785-792
23EEGuido Sanguinetti, Magnus Rattray, Neil D. Lawrence: A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinformatics 22(14): 1753-1759 (2006)
22EEXuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray: Probe-level measurement error improves accuracy in detecting differential gene expression. Bioinformatics 22(17): 2107-2113 (2006)
21EEGuido Sanguinetti, Neil D. Lawrence, Magnus Rattray: Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinformatics 22(22): 2775-2781 (2006)
20EEMagnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence: Propagating uncertainty in microarray data analysis. Briefings in Bioinformatics 7(1): 37-47 (2006)
19EETonatiuh Peña Centeno, Neil D. Lawrence: Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis. Journal of Machine Learning Research 7: 455-491 (2006)
18 Joab Winkler, Mahesan Niranjan, Neil D. Lawrence: Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures Springer 2005
17EEXuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray: A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips. Bioinformatics 21(18): 3637-3644 (2005)
16EEGuido Sanguinetti, Marta Milo, Magnus Rattray, Neil D. Lawrence: Accounting for probe-level noise in principal component analysis of microarray data. Bioinformatics 21(19): 3748-3754 (2005)
15EENeil D. Lawrence: Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. Journal of Machine Learning Research 6: 1783-1816 (2005)
14EEMichael E. Tipping, Neil D. Lawrence: Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing 69(1-3): 123-141 (2005)
13EENeil D. Lawrence, John C. Platt, Michael I. Jordan: Extensions of the Informative Vector Machine. Deterministic and Statistical Methods in Machine Learning 2004: 56-87
12EENeil D. Lawrence, John C. Platt: Learning to learn with the informative vector machine. ICML 2004
11EENeil D. Lawrence, Michael I. Jordan: Semi-supervised Learning via Gaussian Processes. NIPS 2004
10EENeil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier: Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics 20(4): (2004)
9EEJaco Vermaak, Neil D. Lawrence, Patrick Pérez: Variational Inference for Visual Tracking. CVPR (1) 2003: 773-780
8EENeil D. Lawrence: Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. NIPS 2003
7EENeil D. Lawrence, Matthias Seeger, Ralf Herbrich: Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS 2002: 609-616
6EEAntony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop: Probabilistic Modelling of Replica Divergence. HotOS 2001: 55-60
5 Neil D. Lawrence, Bernhard Schölkopf: Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. ICML 2001: 306-313
4EENeil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor: Optimising Synchronisation Times for Mobile Devices. NIPS 2001: 1401-1408
3EEBoaz Lerner, Neil D. Lawrence: A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics. Neural Computing and Applications 10(1): 39-47 (2001)
2EENeil D. Lawrence, Christopher M. Bishop, Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998: 320-327
1 Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997

Coauthor Index

1M. Alkarouri [33]
2Mauricio Alvarez [34]
3Christopher M. Bishop [1] [2] [4] [6]
4Ben Calderhead [35]
5Joaquin Quiñonero Candela [25]
6Tonatiuh Peña Centeno [19]
7Trevor Darrell [38]
8Luka Eciolaza [33]
9Carl Henrik Ek [30] [37]
10Brian Ferris [31]
11David J. Fleet [29] [38]
12Peter J. Fleming [33]
13Dieter Fox [31]
14Pei Gao [39]
15Andreas Geiger [38]
16Mark A. Girolami (Mark Girolami) [35]
17Ralf Herbrich [7]
18Antti Honkela [39]
19Tommi Jaakkola [1]
20Michael I. Jordan [1] [2] [11] [13]
21Visakan Kadirkamanathan [33]
22Nathaniel J. King [27]
23Boaz Lerner [3]
24Xuejun Liu [17] [20] [22] [40]
25Marta Milo [10] [16] [17] [20] [22] [40]
26Andrew J. Moore [32]
27Mahesan Niranjan [10] [18]
28Richard D. Pearson [40]
29Patrick Pérez [9]
30John C. Platt [12] [13]
31Jovan Popovic [38]
32Penny Rashbass [10]
33Magnus Rattray [16] [17] [20] [21] [22] [23] [24] [28] [36] [39] [40]
34Jonathan Rihan [37]
35Grégory Rogez [37]
36Antony I. T. Rowstron [4] [6]
37Guido Sanguinetti [16] [20] [21] [23] [24] [26] [28] [40]
38Bernhard Schölkopf [5]
39Matthias Seeger [7]
40Stephan Soullier [10]
41M. J. Taylor [4]
42Michael E. Tipping [14]
43Michalis Titsias [36]
44Philip H. S. Torr [30] [37]
45Raquel Urtasun [29] [38] [41]
46Jaco Vermaak [9]
47Joab Winkler [18]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)