Neil D. Lawrence

2008
39	EE	Pei 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
38	EE	Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell, Neil D. Lawrence: Topologically-constrained latent variable models. ICML 2008: 1080-1087
37	EE	Carl Henrik Ek, Jonathan Rihan, Philip H. S. Torr, Grégory Rogez, Neil D. Lawrence: Ambiguity Modeling in Latent Spaces. MLMI 2008: 62-73
36	EE	Michalis Titsias, Neil D. Lawrence, Magnus Rattray: Efficient Sampling for Gaussian Process Inference using Control Variables. NIPS 2008: 1681-1688
35	EE	Ben Calderhead, Mark Girolami, Neil D. Lawrence: Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. NIPS 2008: 217-224
34	EE	Mauricio Alvarez, Neil D. Lawrence: Sparse Convolved Gaussian Processes for Multi-output Regression. NIPS 2008: 57-64
2007
33	EE	Luka Eciolaza, M. Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming: Gaussian Process Latent Variable Models for Fault Detection. CIDM 2007: 287-292
32	EE	Neil D. Lawrence, Andrew J. Moore: Hierarchical Gaussian process latent variable models. ICML 2007: 481-488
31	EE	Brian Ferris, Dieter Fox, Neil D. Lawrence: WiFi-SLAM Using Gaussian Process Latent Variable Models. IJCAI 2007: 2480-2485
30	EE	Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrence: Gaussian Process Latent Variable Models for Human Pose Estimation. MLMI 2007: 132-143
29	EE	Raquel Urtasun, David J. Fleet, Neil D. Lawrence: Modeling Human Locomotion with Topologically Constrained Latent Variable Models. Workshop on Human Motion 2007: 104-118
2006
28	EE	Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence: Identifying Submodules of Cellular Regulatory Networks. CMSB 2006: 155-168
27	EE	Nathaniel J. King, Neil D. Lawrence: Fast Variational Inference for Gaussian Process Models Through KL-Correction. ECML 2006: 270-281
26	EE	Guido Sanguinetti, Neil D. Lawrence: Missing Data in Kernel PCA. ECML 2006: 751-758
25	EE	Neil D. Lawrence, Joaquin Quiñonero Candela: Local distance preservation in the GP-LVM through back constraints. ICML 2006: 513-520
24	EE	Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray: Modelling transcriptional regulation using Gaussian Processes. NIPS 2006: 785-792
23	EE	Guido 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)
22	EE	Xuejun 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)
21	EE	Guido Sanguinetti, Neil D. Lawrence, Magnus Rattray: Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinformatics 22(22): 2775-2781 (2006)
20	EE	Magnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence: Propagating uncertainty in microarray data analysis. Briefings in Bioinformatics 7(1): 37-47 (2006)
19	EE	Tonatiuh 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)
2005
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
17	EE	Xuejun 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)
16	EE	Guido 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)
15	EE	Neil D. Lawrence: Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. Journal of Machine Learning Research 6: 1783-1816 (2005)
14	EE	Michael 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)
2004
13	EE	Neil D. Lawrence, John C. Platt, Michael I. Jordan: Extensions of the Informative Vector Machine. Deterministic and Statistical Methods in Machine Learning 2004: 56-87
12	EE	Neil D. Lawrence, John C. Platt: Learning to learn with the informative vector machine. ICML 2004
11	EE	Neil D. Lawrence, Michael I. Jordan: Semi-supervised Learning via Gaussian Processes. NIPS 2004
10	EE	Neil 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)
2003
9	EE	Jaco Vermaak, Neil D. Lawrence, Patrick Pérez: Variational Inference for Visual Tracking. CVPR (1) 2003: 773-780
8	EE	Neil D. Lawrence: Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. NIPS 2003
2002
7	EE	Neil D. Lawrence, Matthias Seeger, Ralf Herbrich: Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS 2002: 609-616
2001
6	EE	Antony 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
4	EE	Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor: Optimising Synchronisation Times for Mobile Devices. NIPS 2001: 1401-1408
3	EE	Boaz 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)
1998
2	EE	Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998: 320-327
1997
1		Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997

Coauthor Index

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

Colors in the list of coauthors