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Publications

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2020

Conference Papers

2019

Journal Papers

    Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance

    Sylvie DelacroixNeil D. Lawrence; International Data Privacy Law, Oxford Academic 9(4):236-252

    Intrinsic Gaussian Processes on Complex Constrained Domains

    Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David Dunson; :

Conference Papers

    Variational Information Distillation for Knowledge Transfer

    Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai; Conference on Computer Vision and Pattern Recognition (CVPR), :9155-9163

    Transferring Knowledge across Learning Processes

    Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas Damianou; International Conference on Learning Representations, :

2018

Journal Papers

Conference Papers

    Structured Variationally Auto-encoded Optimization

    Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil D. Lawrence; Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3273-3281

    Differentially Private Regression with Gaussian Processes

    Michael T. Smith, Mauricio Álvarez, Max Zwiessele, Neil D. Lawrence; Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:1195-1203

2017

Journal Papers

Conference Papers

    Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

    Zhenwen DaiMauricio A. ÁlvarezNeil D. Lawrence; Advances in Neural Information Processing Systems, Curran Associates, Inc. 30:5131-5139

    Preferential Bayesian Optimization

    Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence; Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1282-1291

Technical Reports

2016

Journal Papers

Conference Papers

Technical Reports

2015

Journal Papers

    Genome-wide Modeling of Transcription Kinetics Reveals Patterns of RNA Production Delays

    Antti HonkelaJaakko Peltonen, Hande Topa, Iryna Charapitsa, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. LawrenceMagnus Rattray; Proc. Natl. Acad. Sci. USA, 112(42):13115-13120

    A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data

    Gennaro Gambardella, Ivana Peluso, Sandro Montefusco, Mukesh Bansal, Diego L. Medina, Neil D. Lawrence, Diego Bernardo; BMC Bioinformatics, 16(279):

Conference Papers

    Semi-described and Semi-supervised Learning with Gaussian Processes

    Andreas DamianouNeil D. Lawrence; 31st Conference on Uncertainty in Artificial Intelligence (UAI), :

2014

Journal Papers

Conference Papers

Technical Reports

Miscellaneous

2013

Journal Papers

Miscellaneous

2013

Conference Papers

Miscellaneous

2013

Journal Papers

    Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors

    Nicoló FusiChristoph LippertKarsten BorgwardtNeil D. LawrenceOliver Stegle; Bioinformatics, 29(11):1382-1389

    Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity

    Alice Brockington, Ke Ning, Paul R. Heath, Elizabeth Wood, Janine Kirby, Nicoló FusiNeil D. Lawrence, Stephen B. Wharton, Paul G. Ince, Pamela J. Shaw; Acta Neuropathologica, 125(1):

Conference Papers

Book Chapters

2012

Journal Papers

    Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition

    Christopher A. Penfold, Paul E. Brown, Neil D. Lawrence, Alastair S. H. Goldman; PLoS Computational Biology, 8(5):0-0

    Identifying Targets of Multiple Co-regulated Transcription Factors from Expression Time-series by Bayesian Model Comparison

    Michalis K. TitsiasAntti HonkelaNeil D. LawrenceMagnus Rattray; BMC Systems Biology, 6(53):

    Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies

    Nicoló FusiOliver StegleNeil D. Lawrence; PLoS Computat Biol, Public Library of Science 8:0-0

    Genome-wide occupancy links Hoxa2 to Wnt-$\beta$-catenin signaling in mouse embryonic development

    Ian J. Donaldson, Shilu Amin, James Hensman, Eva Kutejova, Magnus RattrayNeil D. Lawrence, Andrew Hayes, Christopher M. Ward, Nicoletta Bobola; Nucleaic Acids Research, 40(9):3390-4001

    Kernels for Vector-Valued Functions: A Review

    Mauricio A. ÁlvarezLorenzo RosascoNeil D. Lawrence; Foundations and Trends in Machine Learning, 4(3):195-266

    A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models

    Neil D. Lawrence; Journal of Machine Learning Research, 13(51):1609-1638

Conference Papers

Technical Reports

2011

Journal Papers

Conference Papers

Book Chapters

Technical Reports

2010

Journal Papers

    Model-based Method for Transcription Factor Target Identification with Limited Data

    Antti Honkela, Charles Girardot, E. Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. LawrenceMagnus Rattray; Proc. Natl. Acad. Sci. USA, 107(17):7793-7798

    Elementary properties of CaV1.3 Ca2+ channels expressed in mouse cochlear inner hair cells

    Valeria Zampini, Stuart Leigh Johnson, Christoph Franz, Neil D. Lawrence, Stefan Muenkner Jutta Engel, Marlies Knipper, Jacopo Magistretti, Sergio Masetto, Walter Marcotti; The Journal of Physiology, 588:187-189

    TFInfer: a tool for probabilistic inference of transcription factor activities

    H. M. Shahzad Asif, Matthew D. Rolfe, Jeff Green, Neil D. LawrenceMagnus RattrayGuido Sanguinetti; Bioinformatics, 26:2635-2636

Conference Papers

Book Chapters

Technical Reports

    A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction

    Neil D. Lawrence; :

2009

Journal Papers

Conference Papers

    Efficient Sampling for Gaussian Process Inference using Control Variables

    Michalis K. TitsiasNeil D. LawrenceMagnus Rattray; Advances in Neural Information Processing Systems, MIT Press 21:1681-1688

    Non-Linear Matrix Factorization with Gaussian Processes

    Neil D. LawrenceRaquel Urtasun; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 26:

    Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery

    John Darby, Baihua Li, Nicholas Costen, David J. Fleet, Neil D. Lawrence; British Machine Vision Conference, :

    Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

    Ben Calderhead, Mark GirolamiNeil D. Lawrence; Advances in Neural Information Processing Systems, MIT Press 21:217-224

    Latent Force Models

    Mauricio A. ÁlvarezDavid LuengoNeil D. Lawrence; Proceedings of the Twelfth International Workshop on Artificial Intelligence and Statistics, JMLR W\&CP 5 5:9-16

    Sparse Convolved Gaussian Processes for Multi-output Regression

    Mauricio A. ÁlvarezNeil D. Lawrence; Advances in Neural Information Processing Systems, MIT Press 21:57-64

Technical Reports

2008

Journal Papers

    Probabilistic approach to detecting dependencies between data sets

    Aarto Klami, Sami Kaski; Neurocomputing, 72:39-46

    Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities

    Pei GaoAntti HonkelaMagnus RattrayNeil D. Lawrence; Bioinformatics, 24:0-0

Conference Papers

    Topologically-Constrained Latent Variable Models

    Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popović, Trevor J. Darrell, Neil D. Lawrence; Proceedings of the International Conference in Machine Learning, Omnipress 25:1080-1087

    Gaussian Process Latent Variable Models For Human Pose Estimation

    Carl Henrik EkPhilip H. S. TorrNeil D. Lawrence; Machine Learning for Multimodal Interaction (MLMI 2007), Springer-Verlag 4892:132-143

    Ambiguity Modeling in Latent Spaces

    Carl Henrik Ek, Jon Rihan, Philip H. S. Torr, Gregory Rogez, Neil D. Lawrence; Machine Learning for Multimodal Interaction (MLMI 2008), Springer-Verlag:62-73

2007

Conference Papers

    Model-driven detection of Clean Speech Patches in Noise

    Jonathan Laidler, Martin CookeNeil D. Lawrence; Proceedings of Interspeech 2007, :

    Modelling transcriptional regulation using Gaussian Processes

    Neil D. LawrenceGuido SanguinettiMagnus Rattray; Advances in Neural Information Processing Systems, MIT Press 19:785-792

    Learning for Larger Datasets with the Gaussian Process Latent Variable Model

    Neil D. Lawrence; Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics, Omnipress:243-250

    Hierarchical Gaussian Process Latent Variable Models

    Neil D. Lawrence, Andrew J. Moore; Proceedings of the International Conference in Machine Learning, Omnipress 24:481-488

    WiFi-SLAM Using Gaussian Process Latent Variable Models

    Brian D. Ferris, Dieter Fox, Neil D. Lawrence; Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), :2480-2485

    Gaussian Process Latent Variable Models for Fault Detection

    Luka Eciolaza, M. Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming; Computational Intelligence and Data Mining, :287-292

Miscellaneous

2006

Journal Papers

Conference Papers

    Identifying submodules of cellular regulatory networks

    Guido SanguinettiMagnus RattrayNeil D. Lawrence; International Conference on Computational Methods in Systems Biology, Springer-Verlag:

    Missing Data in Kernel PCA

    Guido SanguinettiNeil D. Lawrence; ECML, Berlin, 2006, Springer-Verlag:751-758

    Local Distance Preservation in the GP-LVM through Back Constraints

    Neil D. Lawrence, Joaquin Quiñonero Candela; Proceedings of the International Conference in Machine Learning, Omnipress 23:513-520

    Fast Variational Inference for Gaussian Process Models through KL-Correction

    Nathaniel J. King, Neil D. Lawrence; ECML, Berlin, 2006, Springer-Verlag:270-281

Book Chapters

Technical Reports

2005

Journal Papers

Conference Papers

Book Chapters

    Extensions of the Informative Vector Machine

    Neil D. Lawrence, John C. Platt, Michael I. Jordan; Deterministic and Statistical Methods in Machine Learning, Springer-Verlag 3635:56-87

Technical Reports

    Variational Inference in Gaussian Processes via Probabilistic Point Assimilation

    Nathaniel J. King, Neil D. Lawrence; (CS-05-06):

Miscellaneous

2004

Journal Papers

Conference Papers

    Learning to Learn with the Informative Vector Machine

    Neil D. Lawrence, John C. Platt; Proceedings of the International Conference in Machine Learning, Omnipress 21:512-519

    Gaussian Process Models for Visualisation of High Dimensional Data

    Neil D. Lawrence; Advances in Neural Information Processing Systems, MIT Press 16:329-336

    Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle

    Yasser H. Abdel-HaleemSteve RenalsNeil D. Lawrence; International Conference on Acoustics, Speech and Signal Processing, :

Technical Reports

2003

Journal Papers

Conference Papers

    Variational Inference for Visual Tracking

    Jaco Vermaak, Neil D. Lawrence, Patrick Pérez; Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press I:773-780

    A Variational Approach to Robust Bayesian Interpolation

    Michael E. Tipping, Neil D. Lawrence; Neural Networks for Signal Processing XIII, IEEE:229-238

    Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

    Matthias Seeger, Christopher K. I. Williams, Neil D. Lawrence; Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, :

    Bayesian Processing of Microarray Images

    Neil D. LawrenceMarta MiloMahesan Niranjan, Penny Rashbass, Stephan Soullier; Neural Networks for Signal Processing XIII, IEEE:71-80

    Fast Sparse Gaussian Process Methods: The Informative Vector Machine

    Neil D. Lawrence, Matthias Seeger, Ralf Herbrich; Advances in Neural Information Processing Systems, MIT Press 15:625-632

Technical Reports

2002

Conference Papers

    Optimising Synchronisation Times for Mobile Devices

    Neil D. Lawrence, Anthony I. T. Rowstron, Christopher M. Bishop, Michael J. Taylor; Advances in Neural Information Processing Systems, MIT Press 14:1401-1408

Technical Reports

2001

Journal Papers

    A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics

    Boaz LernerNeil D. Lawrence; Neural Computing and Applications, 10(1):39-47

Conference Papers

    Probabilistic Modelling of Replica Divergence

    Anthony I. T. Rowstron, Neil D. LawrenceChristopher M. Bishop; Proceedings of the 8th Workshop on Hot Topics in Operating Systems HOTOS (VIII), :

    Node Relevance Determination

    Neil D. Lawrence; The {XI}th Italian Workshop on Neural Networks, Springer-Verlag:

    Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

    Neil D. LawrenceBernhard Schölkopf; Proceedings of the International Conference in Machine Learning, Morgan Kauffman 18:

    Variational Learning for Multi-layer networks of Linear Threshold Units

    Neil D. Lawrence; Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, Morgan Kauffman:245-252

Technical Reports

Miscellaneous

2000

Technical Reports

1999

Technical Reports

    A Variational Bayesian Committee of Neural Networks

    Neil D. Lawrence, Mehdi Azzouzi; :

1998

Conference Papers

Technical Reports

0001

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