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Department of Computer Science and Technology

Neil Lawrence

  • The DeepMind Professor of Machine Learning
  • Senior AI Fellow, The Alan Turing Institute
  • Visiting Professor of Machine Learning, University of Sheffield

Neil is a professor of machine learning whose technical expertise is in uncertainty and machine learning methods. With the increased impact of machine learning in society, in particular its use as the principal technology underpinning modern artificial intelligence, Neil has also become interested in public understanding of machine learning, policy decisions around machine learning and the implications for data governance.

As well as his departmental roles he serves on  the board of the conference AISTATS and the ELLIS Foundation, and as the founding and series editor for the Proceedings of Machine Learning Research.

For more details about his activities please check his webpage here.

Research

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the physical world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.

Themes

Teaching

Publications

Journal articles

  • Vargas, F., Ovsianas, A., Fernandes, D., Girolami, M., Lawrence, ND. and Nüsken, N., 2023. Bayesian learning via neural Schrödinger–Föllmer flows Statistics and Computing, v. 33
    Doi: http://doi.org/10.1007/s11222-022-10172-5
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  • Vargas, F., Thodoroff, P., Lamacraft, A. and Lawrence, N., 2023. Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134. Entropy (Basel), v. 25
    Doi: http://doi.org/10.3390/e25020289
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  • Mishra, C., von Wolff, N., Tripathi, A., Brodie, CN., Lawrence, ND., Ravuri, A., Bremond, É., Preiss, A. and Kumar, A., 2023. Predicting ruthenium catalysed hydrogenation of esters using machine learning Digital Discovery, v. 2
    Doi: http://doi.org/10.1039/d3dd00029j
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  • Cortes, C. and Lawrence, ND., 2021. Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment
  • Smith, MT., Álvarez, MA. and Lawrence, ND., 2021. Differentially private regression and classification with sparse Gaussian processes Journal of Machine Learning Research, v. 22
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  • Bell, SJ. and Lawrence, ND., 2021. Behavioral Experiments for Understanding Catastrophic Forgetting Presented at the AI Evaluation Beyond Metrics (EBeM) Workshop at IJCAI, Vienna 2022,
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  • Vargas, F., Ovsianas, A., Fernandes, D., Girolami, M., Lawrence, ND. and Nüsken, N., 2021. Bayesian Learning via Neural Schrödinger-Föllmer Flows
  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: 10.1109/TII.2019.2942650
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  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
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  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
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  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
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  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
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  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2019. A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant Applied Soft Computing, v. 82
    Doi: http://doi.org/10.1016/j.asoc.2019.105527
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  • Niu, M., Cheung, P., Lin, L., Dai, Z., Lawrence, N. and Dunson, D., 2019. Intrinsic Gaussian processes on complex constrained domains Journal of the Royal Statistical Society: Series B (Statistical Methodology), v. 81
    Doi: http://doi.org/10.1111/rssb.12320
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  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
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  • Prescott, AJ., Camilleri, D., martinez-hernandez, U., Damianou, A. and Lawrence, N., 2019. Memory and Mental Time Travel in Humans and Social Robots Philosophical Transactions B, v. 374
    Doi: http://doi.org/10.1098/rstb.2018.0025
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  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
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  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
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  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
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  • Särkkä, S., Álvarez, MA. and Lawrence, ND., 2018. Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems IEEE Transactions on Automatic Control, v. 64
    Doi: http://doi.org/10.1109/TAC.2018.2874749
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  • Lake, BM., Lawrence, ND. and Tenenbaum, JB., 2018. The Emergence of Organizing Structure in Conceptual Representation Cognitive Science, v. 42
    Doi: http://doi.org/10.1111/cogs.12580
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  • Dai, Z., Iqbal, M., Lawrence, ND. and Rattray, M., 2017. Efficient inference for sparse latent variable models of transcriptional regulation Bioinformatics, v. 33
    Doi: http://doi.org/10.1093/bioinformatics/btx508
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  • Dai, Z., Iqbal, M., Lawrence, ND. and Rattray, M., 2017. Efficient inference for sparse latent variable models of transcriptional regulation Bioinformatics, v. 33
    Doi: http://doi.org/10.1093/bioinformatics/btx508
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  • Perdikaris, P., Raissi, M., Damianou, A., Lawrence, ND. and Karniadakis, GE., 2017. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, v. 473
    Doi: http://doi.org/10.1098/rspa.2016.0751
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  • Perdikaris, P., Raissi, M., Damianou, A., Lawrence, ND. and Karniadakis, GE., 2017. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, v. 473
    Doi: http://doi.org/10.1098/rspa.2016.0751
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  • Mattos, CLC., Dai, Z., Damianou, A., Barreto, GA. and Lawrence, ND., 2017. Deep recurrent Gaussian processes for outlier-robust system identification Journal of Process Control, v. 60
    Doi: http://doi.org/10.1016/j.jprocont.2017.06.010
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  • Mattos, CLC., Dai, Z., Damianou, A., Barreto, GA. and Lawrence, ND., 2017. Deep recurrent Gaussian processes for outlier-robust system identification Journal of Process Control, v. 60
    Doi: http://doi.org/10.1016/j.jprocont.2017.06.010
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  • Lönnberg, T., Svensson, V., James, KR., Fernandez-Ruiz, D., Sebina, I., Montandon, R., Soon, MSF., Fogg, LG., Nair, AS., Liligeto, UN., Stubbington, MJT., Ly, L-H., Bagger, FO., Zwiessele, M., Lawrence, ND., Souza-Fonseca-Guimaraes, F., Bunn, PT., Engwerda, CR., Heath, WR., Billker, O., Stegle, O., Haque, A. and Teichmann, SA., 2017. Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves TH1/TFH fate bifurcation in malaria Science Immunology, v. 2
    Doi: http://doi.org/10.1126/sciimmunol.aal2192
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  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., 2016. Detecting periodicities with Gaussian processes PeerJ Computer Science, v. 2
    Doi: http://doi.org/10.7717/peerj-cs.50
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  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., 2016. Detecting periodicities with Gaussian processes PeerJ Computer Science, v. 2
    Doi: http://doi.org/10.7717/peerj-cs.50
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  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2016. Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes Journal of Machine Learning Research, v. 17
    Doi: http://doi.org/10.5555/2946645.2946687
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  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2016. Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes Journal of Machine Learning Research, v. 17
    Doi: http://doi.org/10.5555/2946645.2946687
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  • Mattos, CLC., Damianou, A., Barreto, GA. and Lawrence, ND., 2016. Latent Autoregressive Gaussian Processes Models for Robust System Identification IFAC-PapersOnLine, v. 49
    Doi: http://doi.org/10.1016/j.ifacol.2016.07.353
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  • Mattos, CLC., Damianou, A., Barreto, GA. and Lawrence, ND., 2016. Latent Autoregressive Gaussian Processes Models for Robust System Identification IFAC-PapersOnLine, v. 49
    Doi: http://doi.org/10.1016/j.ifacol.2016.07.353
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  • Mattos, CLC., Damianou, A., Barreto, GA. and Lawrence, ND., 2016. Latent Autoregressive Gaussian Processes Models for Robust System Identification IFAC-PapersOnLine, v. 49
    Doi: http://doi.org/10.1016/j.ifacol.2016.07.353
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  • Hensman, J., Rattray, M. and Lawrence, ND., 2015. Fast Nonparametric Clustering of Structured Time-Series IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 37
    Doi: http://doi.org/10.1109/tpami.2014.2318711
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  • Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2015. Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays Proceedings of the National Academy of Sciences, v. 112
    Doi: http://doi.org/10.1073/pnas.1420404112
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  • Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2015. Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays Proceedings of the National Academy of Sciences, v. 112
    Doi: http://doi.org/10.1073/pnas.1420404112
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  • Hensman, J., Rattray, M. and Lawrence, ND., 2015. Fast Nonparametric Clustering of Structured Time-Series IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 37
    Doi: http://doi.org/10.1109/tpami.2014.2318711
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  • Gambardella, G., Peluso, I., Montefusco, S., Bansal, M., Medina, DL., Lawrence, N. and di Bernardo, D., 2015. A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data BMC Bioinformatics, v. 16
    Doi: http://doi.org/10.1186/s12859-015-0700-3
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  • Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2015. Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays Proceedings of the National Academy of Sciences, v. 112
    Doi: http://doi.org/10.1073/pnas.1420404112
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  • Gambardella, G., Peluso, I., Montefusco, S., Bansal, M., Medina, DL., Lawrence, N. and di Bernardo, D., 2015. A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data BMC Bioinformatics, v. 16
    Doi: http://doi.org/10.1186/s12859-015-0700-3
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  • Gambardella, G., Peluso, I., Montefusco, S., Bansal, M., Medina, DL., Lawrence, N. and di Bernardo, D., 2015. A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data BMC Bioinformatics, v. 16
    Doi: http://doi.org/10.1186/s12859-015-0700-3
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  • Hensman, J., Rattray, M. and Lawrence, ND., 2015. Fast Nonparametric Clustering of Structured Time-Series IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 37
    Doi: http://doi.org/10.1109/tpami.2014.2318711
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  • wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
    Doi: http://doi.org/10.1371/journal.pcbi.1003598
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  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
    Doi: http://doi.org/10.1038/ncomms5890
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  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, ND., 2014. Consistent mapping of government malaria records across a changing territory delimitation Malaria Journal, v. 13
    Doi: http://doi.org/10.1186/1475-2875-13-s1-p5
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  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, ND., 2014. Consistent mapping of government malaria records across a changing territory delimitation Malaria Journal, v. 13
    Doi: http://doi.org/10.1186/1475-2875-13-s1-p5
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  • wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
    Doi: http://doi.org/10.1371/journal.pcbi.1003598
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  • wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
    Doi: http://doi.org/10.1371/journal.pcbi.1003598
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  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, ND., 2014. Consistent mapping of government malaria records across a changing territory delimitation Malaria Journal, v. 13
    Doi: http://doi.org/10.1186/1475-2875-13-s1-p5
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  • wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
    Doi: http://doi.org/10.1371/journal.pcbi.1003598
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  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
    Doi: http://doi.org/10.1038/ncomms5890
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  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
    Doi: http://doi.org/10.1038/ncomms5890
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  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
    Doi: http://doi.org/10.1038/ncomms5890
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  • Hensman, J., Lawrence, ND. and Rattray, M., 2013. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters BMC Bioinformatics, v. 14
    Doi: http://doi.org/10.1186/1471-2105-14-252
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  • Brockington, A., Ning, K., Heath, PR., Wood, E., Kirby, J., Fusi, N., Lawrence, N., Wharton, SB., Ince, PG. and Shaw, PJ., 2013. 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 Acta Neuropathologica, v. 125
    Doi: http://doi.org/10.1007/s00401-012-1058-5
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  • Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data Methods in Molecular Biology, v. 939
    Doi: http://doi.org/10.1007/978-1-62703-107-3-6
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  • Brockington, A., Ning, K., Heath, PR., Wood, E., Kirby, J., Fusi, N., Lawrence, N., Wharton, SB., Ince, PG. and Shaw, PJ., 2013. 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 Acta Neuropathologica, v. 125
    Doi: http://doi.org/10.1007/s00401-012-1058-5
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  • Brockington, A., Ning, K., Heath, PR., Wood, E., Kirby, J., Fusi, N., Lawrence, N., Wharton, SB., Ince, PG. and Shaw, PJ., 2013. 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 Acta Neuropathologica, v. 125
    Doi: http://doi.org/10.1007/s00401-012-1058-5
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  • Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data Methods in Molecular Biology, v. 939
    Doi: http://doi.org/10.1007/978-1-62703-107-3-6
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  • Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
    Doi: http://doi.org/10.1109/tpami.2013.86
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  • Nardo, G., Iennaco, R., Fusi, N., Heath, PR., Marino, M., Trolese, MC., Ferraiuolo, L., Lawrence, N., Shaw, PJ. and Bendotti, C., 2013. Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis Brain, v. 136
    Doi: http://doi.org/10.1093/brain/awt250
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  • Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data Methods in Molecular Biology, v. 939
    Doi: http://doi.org/10.1007/978-1-62703-107-3-6
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  • Nardo, G., Iennaco, R., Fusi, N., Heath, PR., Marino, M., Trolese, MC., Ferraiuolo, L., Lawrence, N., Shaw, PJ. and Bendotti, C., 2013. Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis Brain, v. 136
    Doi: http://doi.org/10.1093/brain/awt250
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  • Nardo, G., Iennaco, R., Fusi, N., Heath, PR., Marino, M., Trolese, MC., Ferraiuolo, L., Lawrence, N., Shaw, PJ. and Bendotti, C., 2013. Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis Brain, v. 136
    Doi: http://doi.org/10.1093/brain/awt250
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  • Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
    Doi: http://doi.org/10.1109/tpami.2013.86
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  • Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
    Doi: http://doi.org/10.1109/tpami.2013.86
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  • Fusi, N., Lippert, C., Borgwardt, K., Lawrence, ND. and Stegle, O., 2013. Detecting regulatory gene–environment interactions with unmeasured environmental factors Bioinformatics, v. 29
    Doi: http://doi.org/10.1093/bioinformatics/btt148
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  • Fusi, N., Lippert, C., Borgwardt, K., Lawrence, ND. and Stegle, O., 2013. Detecting regulatory gene–environment interactions with unmeasured environmental factors Bioinformatics, v. 29
    Doi: http://doi.org/10.1093/bioinformatics/btt148
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  • Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
    Doi: http://doi.org/10.1109/tpami.2013.86
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  • Fusi, N., Lippert, C., Borgwardt, K., Lawrence, ND. and Stegle, O., 2013. Detecting regulatory gene–environment interactions with unmeasured environmental factors Bioinformatics, v. 29
    Doi: http://doi.org/10.1093/bioinformatics/btt148
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  • Hensman, J., Lawrence, ND. and Rattray, M., 2013. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters BMC Bioinformatics, v. 14
    Doi: http://doi.org/10.1186/1471-2105-14-252
    •
  • Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
    Doi: http://doi.org/10.1109/tpami.2013.86
    •
  • Hensman, J., Lawrence, ND. and Rattray, M., 2013. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters BMC Bioinformatics, v. 14
    Doi: http://doi.org/10.1186/1471-2105-14-252
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  • Hammer, B., Keim, D., Lawrence, N. and Lebanon, G., 2013. Preface: Intelligent interactive data visualization Data Mining and Knowledge Discovery, v. 27
    Doi: http://doi.org/10.1007/s10618-013-0309-y
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  • Hammer, B., Keim, D., Lawrence, N. and Lebanon, G., 2013. Preface: Intelligent interactive data visualization Data Mining and Knowledge Discovery, v. 27
    Doi: http://doi.org/10.1007/s10618-013-0309-y
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  • Penfold, CA., Brown, PE., Lawrence, ND. and Goldman, ASH., 2012. Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition PLoS Computational Biology, v. 8
    Doi: http://doi.org/10.1371/journal.pcbi.1002496
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  • Penfold, CA., Brown, PE., Lawrence, ND. and Goldman, ASH., 2012. Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition PLoS Computational Biology, v. 8
    Doi: http://doi.org/10.1371/journal.pcbi.1002496
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  • Donaldson, IJ., Amin, S., Hensman, JJ., Kutejova, E., Rattray, M., Lawrence, N., Hayes, A., Ward, CM. and Bobola, N., 2012. Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development Nucleic Acids Research, v. 40
    Doi: http://doi.org/10.1093/nar/gkr1240
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  • Titsias, MK., Honkela, A., Lawrence, ND. and Rattray, M., 2012. Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison BMC Systems Biology, v. 6
    Doi: http://doi.org/10.1186/1752-0509-6-53
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  • Donaldson, IJ., Amin, S., Hensman, JJ., Kutejova, E., Rattray, M., Lawrence, N., Hayes, A., Ward, CM. and Bobola, N., 2012. Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development Nucleic Acids Research, v. 40
    Doi: http://doi.org/10.1093/nar/gkr1240
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  • Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
    Doi: 10.1109/TPAMI.2012.14
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  • Fusi, N., Stegle, O. and Lawrence, ND., 2012. Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies PLoS Computational Biology, v. 8
    Doi: http://doi.org/10.1371/journal.pcbi.1002330
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  • Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
    Doi: http://doi.org/10.1109/TPAMI.2012.14
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  • Fusi, N., Stegle, O. and Lawrence, ND., 2012. Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies PLoS Computational Biology, v. 8
    Doi: http://doi.org/10.1371/journal.pcbi.1002330
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  • Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
    Doi: http://doi.org/10.1109/TPAMI.2012.14
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  • Penfold, CA., Brown, PE., Lawrence, ND. and Goldman, ASH., 2012. Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition PLoS Computational Biology, v. 8
    Doi: http://doi.org/10.1371/journal.pcbi.1002496
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  • Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
    Doi: http://doi.org/10.1109/TPAMI.2012.14
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  • Donaldson, IJ., Amin, S., Hensman, JJ., Kutejova, E., Rattray, M., Lawrence, N., Hayes, A., Ward, CM. and Bobola, N., 2012. Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development Nucleic Acids Research, v. 40
    Doi: http://doi.org/10.1093/nar/gkr1240
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  • Lázaro-Gredilla, M., Van Vaerenbergh, S. and Lawrence, ND., 2012. Overlapping Mixtures of Gaussian Processes for the data association problem Pattern Recognition, v. 45
    Doi: http://doi.org/10.1016/j.patcog.2011.10.004
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  • Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Editor's note IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 34
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  • Milo, M., Fazeli, A., Niranjan, M. and Lawrence, ND., 2003. A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays Biochemical Transations, v. 31
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  • Lerner, B. and Lawrence, ND., 2001. A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics Neural Computing and Applications, v. 10
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  • Zwiessele, M. and Lawrence, ND., Topslam: Waddington Landscape Recovery for Single Cell Experiments
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  • Lawrence, ND., Data Readiness Levels
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
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  • Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
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  • Lawrence, ND., Data Readiness Levels
  • Lawrence, ND., Data Readiness Levels
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis

    • Conference proceedings

  • Paleyes, A. and Lawrence, ND., 2023. Causal fault localisation in dataflow systems Proceedings of the 3rd Workshop on Machine Learning and Systems,
    Doi: http://doi.org/10.1145/3578356.3592593
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  • Paleyes, A., Guo, S., Schölkopf, B. and Lawrence, ND., 2023. Dataflow graphs as complete causal graphs Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023,
    Doi: http://doi.org/10.1109/CAIN58948.2023.00010
    •
  • Li, S., López-García, M., Lawrence, ND. and Cutillo, L., 2022. Two-way Sparse Network Inference for Count Data Proceedings of Machine Learning Research, v. 151
    •
  • Bell, SJ., Kampman, OP., Dodge, J. and Lawrence, ND., 2022. Modeling the Machine Learning Multiverse Advances in Neural Information Processing Systems, v. 35
    •
  • Lalchand, V., Ravuri, A., Dann, E., Kumasaka, N., Sumanaweera, D., Lindeboom, RGH., Madad, S., Teichmann, SA. and Lawrence, ND., 2022. Modelling Technical and Biological Effects in single-cell RNA-seq data with Scalable Gaussian Process Latent Variable Models (GPLVMs) Proceedings of Machine Learning Research, v. 200
    •
  • Thodoroff, P., Li, W. and Lawrence, ND., 2022. Benchmarking Real-Time Reinforcement Learning Proceedings of Machine Learning Research, v. 181
    •
  • Paleyes, A., Cabrera, C. and Lawrence, ND., 2022. An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022,
    Doi: http://doi.org/10.1145/3522664.3528601
    •
  • Lalchand, V., Ravuri, A. and Lawrence, ND., 2022. Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference Proceedings of Machine Learning Research, v. 151
    •
  • Hu, SX., Moreno, PG., Xiao, Y., Shen, X., Obozinski, G., Lawrence, ND. and Damianou, A., 2020. EMPIRICAL BAYES TRANSDUCTIVE META-LEARNING WITH SYNTHETIC GRADIENTS 8th International Conference on Learning Representations, ICLR 2020,
    •
  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: 10.1109/CVPR.2019.00938
    •
  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
    •
  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
    •
  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
    •
  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
    •
  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
    •
  • Klein, A., Dai, Z., Hutter, F., Lawrence, N. and González, J., 2019. Meta-surrogate benchmarking for hyperparameter optimization Advances in Neural Information Processing Systems, v. 32
    •
  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
    •
  • Klein, A., Dai, Z., Hutter, F., Lawrence, N. and González, J., 2019. Meta-surrogate benchmarking for hyperparameter optimization Advances in Neural Information Processing Systems, v. 32
    •
  • Klein, A., Dai, Z., Hutter, F., Lawrence, N. and González, J., 2019. Meta-surrogate benchmarking for hyperparameter optimization Advances in Neural Information Processing Systems, v. 32
    •
  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: http://doi.org/10.1109/CVPR.2019.00938
    •
  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: http://doi.org/10.1109/CVPR.2019.00938
    •
  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: http://doi.org/10.1109/CVPR.2019.00938
    •
  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: http://doi.org/10.1109/CVPR.2019.00938
    •
  • Lu, X., González, J., Dai, Z. and Lawrence, ND., 2018. Structured variationally auto-encoded optimization 35th International Conference on Machine Learning, ICML 2018, v. 7
    •
  • Smith, MT., Álvarez, MA., Zwiessele, M. and Lawrence, ND., 2018. Differentially private regression with gaussian processes International Conference on Artificial Intelligence and Statistics, AISTATS 2018,
    •
  • Smith, MT., Álvarez, MA., Zwiessele, M. and Lawrence, ND., 2018. Differentially private regression with gaussian processes International Conference on Artificial Intelligence and Statistics, AISTATS 2018,
    •
  • Smith, MT., Álvarez, MA., Zwiessele, M. and Lawrence, ND., 2018. Differentially private regression with gaussian processes International Conference on Artificial Intelligence and Statistics, AISTATS 2018,
    •
  • Smith, MT., Álvarez, MA., Zwiessele, M. and Lawrence, ND., 2018. Differentially private regression with gaussian processes International Conference on Artificial Intelligence and Statistics, AISTATS 2018,
    •
  • Smith, MT., Álvarez, MA., Zwiessele, M. and Lawrence, ND., 2018. Differentially private regression with gaussian processes International Conference on Artificial Intelligence and Statistics, AISTATS 2018,
    •
  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
    •
  • Grigorievskiy, A., Lawrence, N. and Sarkka, S., 2017. Parallelizable sparse inverse formulation Gaussian processes (SpInGP) 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP),
    Doi: http://doi.org/10.1109/mlsp.2017.8168130
    •
  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
    •
  • Gonzalez, J., Dai, Z., Damianou, A. and Lawrence, ND., 2017. Preferential Bayesian Optimization 34th International Conference on Machine Learning, ICML 2017, v. 3
    •
  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
    •
  • Gonzalez, J., Dai, Z., Damianou, A. and Lawrence, ND., 2017. Preferential Bayesian Optimization 34th International Conference on Machine Learning, ICML 2017, v. 3
    •
  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
    •
  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2016. Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes
    Doi: http://doi.org/10.1007/978-3-319-44412-3_7
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • González, J., Osborne, M. and Lawrence, ND., 2016. GLASSES: Relieving the myopia of Bayesian optimisation Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Camilleri, D., Damianou, A., Jackson, H., Lawrence, N. and Prescott, T., 2016. iCub Visual Memory Inspector: Visualising the iCub’s Thoughts
    Doi: http://doi.org/10.1007/978-3-319-42417-0_5
    •
  • González, J., Osborne, M. and Lawrence, ND., 2016. GLASSES: Relieving the myopia of Bayesian optimisation Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Saul, AD., Hensman, J., Vehtari, A. and Lawrence, ND., 2016. Chained Gaussian processes Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • González, J., Osborne, M. and Lawrence, ND., 2016. GLASSES: Relieving the myopia of Bayesian optimisation Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Camilleri, D., Damianou, A., Jackson, H., Lawrence, N. and Prescott, T., 2016. iCub Visual Memory Inspector: Visualising the iCub’s Thoughts
    Doi: http://doi.org/10.1007/978-3-319-42417-0_5
    •
  • González, J., Dai, Z., Hennig, P. and Lawrence, N., 2016. Batch bayesian optimization via local penalization Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Camilleri, D., Damianou, A., Jackson, H., Lawrence, N. and Prescott, T., 2016. iCub Visual Memory Inspector: Visualising the iCub’s Thoughts
    Doi: http://doi.org/10.1007/978-3-319-42417-0_5
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Saul, AD., Hensman, J., Vehtari, A. and Lawrence, ND., 2016. Chained Gaussian processes Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Saul, AD., Hensman, J., Vehtari, A. and Lawrence, ND., 2016. Chained Gaussian processes Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • González, J., Dai, Z., Hennig, P. and Lawrence, N., 2016. Batch bayesian optimization via local penalization Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Martinez-Hernandez, U., Damianou, A., Camilleri, D., Boorman, LW., Lawrence, N. and Prescott, TJ., 2016. An integrated probabilistic framework for robot perception, learning and memory 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO),
    Doi: http://doi.org/10.1109/robio.2016.7866589
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Martinez-Hernandez, U., Damianou, A., Camilleri, D., Boorman, LW., Lawrence, N. and Prescott, TJ., 2016. An integrated probabilistic framework for robot perception, learning and memory 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO),
    Doi: http://doi.org/10.1109/robio.2016.7866589
    •
  • Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • González, J., Dai, Z., Hennig, P. and Lawrence, N., 2016. Batch bayesian optimization via local penalization Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
    •
  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2015. Monitoring short term changes of malaria incidence in Uganda with Gaussian processes CEUR Workshop Proceedings, v. 1425
    •
  • Damianou, A. and Lawrence, ND., 2015. Semi-described and semi-supervised learning with Gaussian processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015,
    •
  • Damianou, A. and Lawrence, ND., 2015. Semi-described and semi-supervised learning with Gaussian processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015,
    •
  • Damianou, A. and Lawrence, ND., 2015. Semi-described and semi-supervised learning with Gaussian processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015,
    •
  • Vanschoren, J., Bischl, B., Hutter, F., Sebag, M., Kegl, B., Schmid, M., Napolitano, G., Wolstencroft, K., Williams, AR. and Lawrence, N., 2015. Towards a data science collaboratory Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9385
    •
  • Vanschoren, J., Bischl, B., Hutter, F., Sebag, M., Kegl, B., Schmid, M., Napolitano, G., Wolstencroft, K., Williams, AR. and Lawrence, N., 2015. Towards a data science collaboratory Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9385
    •
  • Vanschoren, J., Bischl, B., Hutter, F., Sebag, M., Kegl, B., Schmid, M., Napolitano, G., Wolstencroft, K., Williams, AR. and Lawrence, N., 2015. Towards a data science collaboratory Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9385
    •
  • Vanschoren, J., Bischl, B., Hutter, F., Sebag, M., Kegl, B., Schmid, M., Napolitano, G., Wolstencroft, K., Williams, AR. and Lawrence, N., 2015. Towards a data science collaboratory Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9385
    •
  • Damianou, A. and Lawrence, ND., 2015. Semi-described and semi-supervised learning with Gaussian processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015,
    •
  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2015. Monitoring short term changes of malaria incidence in Uganda with Gaussian processes CEUR Workshop Proceedings, v. 1425
    •
  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2015. Monitoring short term changes of malaria incidence in Uganda with Gaussian processes CEUR Workshop Proceedings, v. 1425
    •
  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2015. Monitoring short term changes of malaria incidence in Uganda with Gaussian processes CEUR Workshop Proceedings, v. 1425
    •
  • Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
    •
  • Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
    •
  • Tosi, A., Hauberg, S., Vellido, A. and Lawrence, ND., 2014. Metrics for probabilistic geometries Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014,
    •
  • Andrade-Pacheco, R., Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Hybrid discriminative-generative approach with Gaussian processes Journal of Machine Learning Research, v. 33
    •
  • Andrade-Pacheco, R., Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Hybrid discriminative-generative approach with Gaussian processes Journal of Machine Learning Research, v. 33
    •
  • Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
    •
  • Tosi, A., Hauberg, S., Vellido, A. and Lawrence, ND., 2014. Metrics for probabilistic geometries Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014,
    •
  • Andrade-Pacheco, R., Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Hybrid discriminative-generative approach with Gaussian processes Journal of Machine Learning Research, v. 33
    •
  • Cohn, T., Preotiuc-Pietro, D. and Lawrence, N., 2014. Gaussian Processes for Natural Language Processing Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials,
    Doi: 10.3115/v1/p14-6001
    •
  • Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
    •
  • Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
    •
  • Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
    •
  • Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
    •
  • Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
    •
  • Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
    •
  • Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
    •
  • Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
    •
  • Lee, WB., Ojovan, M. and Jantzen, CM., 2013. Preface
    Doi: http://doi.org/10.1016/b978-0-85709-435-3.50033-1
    •
  • Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
    •
  • Damianou, AC., Ek, CH., Titsias, MK. and Lawrence, ND., 2012. Manifold relevance determination Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
    •
  • Kalaitzis, AA. and Lawrence, ND., 2012. Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models Proceedings of the 29th International Coference on International Conference on Machine Learning,
    •
  • Damianou, AC., Ek, CH., Titsias, MK. and Lawrence, ND., 2012. Manifold relevance determination Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
    •
  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
    •
  • Maxwell, JR., Taylor, LH., Pachecho, RA., Lawrence, N., Duff, GW., Teare, MD. and Wilson, AG., 2012. Inverse Relation Between the tumor Necrosis Factor Promoter Methylation and Trascript Leveles in Leukocytes From Patients with Rheumatoid Arthritis. ARTHRITIS AND RHEUMATISM, v. 64
    •
  • Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
    •
  • Damianou, AC., Ek, CH., Titsias, MK. and Lawrence, ND., 2012. Manifold relevance determination Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
    •
  • Kalaitzis, AA. and Lawrence, ND., 2012. Residual component analysis: Generalising PCA for more flexible inference in linear-Gaussian models Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
    •
  • Maxwell, JR., Taylor, LH., Pachecho, RA., Lawrence, N., Duff, GW., Teare, MD. and Wilson, AG., 2012. Inverse Relation Between the tumor Necrosis Factor Promoter Methylation and Trascript Leveles in Leukocytes From Patients with Rheumatoid Arthritis. ARTHRITIS AND RHEUMATISM, v. 64
    •
  • Kalaitzis, AA. and Lawrence, ND., 2012. Residual component analysis: Generalising PCA for more flexible inference in linear-Gaussian models Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
    •
  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
    •
  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
    •
  • Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
    •
  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
    •
  • Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
    •
  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
    •
  • Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K., 2011. Efficient inference in matrix-variate Gaussian models with iid observation noise Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K., 2011. Efficient inference in matrix-variate Gaussian models with iid observation noise Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
    •
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
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  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
    •
  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
    •
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K., 2011. Efficient inference in matrix-variate Gaussian models with iid observation noise Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K., 2011. Efficient inference in matrix-variate Gaussian models with iid observation noise Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K., 2011. Efficient inference in matrix-variate Gaussian models with iid observation noise Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
    •
  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
    •
  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
    •
  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
    •
  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
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  • Titsias, MK., Lawrence, ND. and Rattray, M., 2009. Efficient sampling for Gaussian process inference using control variables Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • Titsias, MK., Lawrence, ND. and Rattray, M., 2009. Efficient sampling for Gaussian process inference using control variables Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
    •
  • Titsias, MK., Lawrence, ND. and Rattray, M., 2009. Efficient sampling for Gaussian process inference using control variables Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
    •
  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
    •
  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
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  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
    •
  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
    •
  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
    •
  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
    •
  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
    •
  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
    •
  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
    •
  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
    •
  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
    •
  • Laidler, J., Cooke, M. and Lawrence, ND., 2007. Model-driven detection of clean speech patches in noise International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007, v. 3
    •
  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
    •
  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
    •
  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
    •
  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
    •
  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
    •
  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
    •
  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
    •
  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
    •
  • Laidler, J., Cooke, M. and Lawrence, ND., 2007. Model-driven detection of clean speech patches in noise International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007, v. 3
    •
  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
    •
  • Laidler, J., Cooke, M. and Lawrence, ND., 2007. Model-driven detection of clean speech patches in noise International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007, v. 3
    •
  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
    •
  • Laidler, J., Cooke, M. and Lawrence, ND., 2007. Model-driven detection of clean speech patches in noise International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007, v. 3
    •
  • Laidler, J., Cooke, M. and Lawrence, ND., 2007. Model-driven detection of clean speech patches in noise International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007, v. 3
    •
  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
    •
  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
    •
  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
    •
  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
    •
  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
    •
  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
    •
  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2006. Modelling transcriptional regulation using Gaussian processes NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems,
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
    •
  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
    •
  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
    •
  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
    •
  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
    •
  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
    •
  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
    •
  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
    •
  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
    •
  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
    •
  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, N., Seeger, M. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Rahman, MA. and Lawrence, ND., A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB ’16,
    Doi: http://doi.org/10.1145/2975167.2985651
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Rahman, MA. and Lawrence, ND., A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB ’16,
    Doi: http://doi.org/10.1145/2975167.2985651
    •

    Internet publications

  • Paleyes, A., Urma, R-G. and Lawrence, ND., 2020. Challenges in Deploying Machine Learning: a Survey of Case Studies

    • Other publications

  • 2018. Soon, L. G. Fogg, A. S. Nair, U. N. Liligeto, M. J. T. Stubbington, L.-H. Ly, F. Otzen Bagger, M. Zwiessele, N. D. Lawrence, F. Souza-Fonseca-Guimaraes, P. T. Bunn, C. R. Engwerda, W. R. Heath, O. Billker, O. Stegle, A. Haque, S. A. Teichmann. ... Sci Immunol, v. 3
    Doi: http://doi.org/10.1126/sciimmunol.aat1469
    •
  • 2018. Soon, L. G. Fogg, A. S. Nair, U. N. Liligeto, M. J. T. Stubbington, L.-H. Ly, F. Otzen Bagger, M. Zwiessele, N. D. Lawrence, F. Souza-Fonseca-Guimaraes, P. T. Bunn, C. R. Engwerda, W. R. Heath, O. Billker, O. Stegle, A. Haque, S. A. Teichmann. ... Sci Immunol, v. 3
    Doi: http://doi.org/10.1126/sciimmunol.aat1469
    •
  • 2018. Soon, L. G. Fogg, A. S. Nair, U. N. Liligeto, M. J. T. Stubbington, L.-H. Ly, F. Otzen Bagger, M. Zwiessele, N. D. Lawrence, F. Souza-Fonseca-Guimaraes, P. T. Bunn, C. R. Engwerda, W. R. Heath, O. Billker, O. Stegle, A. Haque, S. A. Teichmann. ... Sci Immunol, v. 3
    Doi: http://doi.org/10.1126/sciimmunol.aat1469
    •
  • 2018. Soon, L. G. Fogg, A. S. Nair, U. N. Liligeto, M. J. T. Stubbington, L.-H. Ly, F. Otzen Bagger, M. Zwiessele, N. D. Lawrence, F. Souza-Fonseca-Guimaraes, P. T. Bunn, C. R. Engwerda, W. R. Heath, O. Billker, O. Stegle, A. Haque, S. A. Teichmann. ... Sci Immunol, v. 3
    Doi: http://doi.org/10.1126/sciimmunol.aat1469
    •
  • Lawrence, ND., 2010. A Probabilistic Perspective on Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Probabilistic Perspective on Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Probabilistic Perspective on Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Probabilistic Perspective on Spectral Dimensionality Reduction
  • Lawrence, ND., 2007. Variational Optimisation by Marginal Matching
  • Lawrence, ND., 2007. Variational Optimisation by Marginal Matching
  • Lawrence, ND., 2007. Variational Optimisation by Marginal Matching
  • Lawrence, ND., 2007. Variational Optimisation by Marginal Matching
  • Lawrence, ND., 2007. Variational Optimisation by Marginal Matching
  • Lawrence, ND., 2007. Variational Optimisation by Marginal Matching
  • Lawrence, ND. and Milo, M., 2003. Variational Importance Sampling
  • Lawrence, ND. and Milo, M., 2003. Variational Importance Sampling
  • Lawrence, ND., 2003. Particle Filters, Variational methods and Importance Sampling
  • Lawrence, ND. and Milo, M., 2003. Variational Importance Sampling
  • Lawrence, ND., 2003. Particle Filters, Variational methods and Importance Sampling
  • Lawrence, ND., 2003. Particle Filters, Variational methods and Importance Sampling
  • Lawrence, ND., 2003. Particle Filters, Variational methods and Importance Sampling
  • Hensman, J. and Lawrence, ND., Nested Variational Compression in Deep Gaussian Processes
  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., Gaussian process models for periodicity detection
  • Smith, MT., Zwiessele, M. and Lawrence, ND., Differentially Private Gaussian Processes
  • Damianou, AC., Titsias, MK. and Lawrence, ND., Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
  • Hensman, J. and Lawrence, ND., Nested Variational Compression in Deep Gaussian Processes
  • Smith, MT., Zwiessele, M. and Lawrence, ND., Differentially Private Gaussian Processes
  • Hensman, J. and Lawrence, ND., Nested Variational Compression in Deep Gaussian Processes
  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., Genetic Analysis of Transformed Phenotypes
  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., Gaussian process models for periodicity detection
  • Smith, MT., Zwiessele, M. and Lawrence, ND., Differentially Private Gaussian Processes
  • Hensman, J. and Lawrence, ND., Nested Variational Compression in Deep Gaussian Processes
  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., Gaussian process models for periodicity detection
  • Smith, MT., Zwiessele, M. and Lawrence, ND., Differentially Private Gaussian Processes
  • Damianou, AC., Titsias, MK. and Lawrence, ND., Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
  • Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
  • Smith, MT., Zwiessele, M. and Lawrence, ND., Differentially Private Gaussian Processes
  • Damianou, AC., Titsias, MK. and Lawrence, ND., Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
  • Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
  • Damianou, AC., Titsias, MK. and Lawrence, ND., Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
  • Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
  • Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., Genetic Analysis of Transformed Phenotypes
  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., Genetic Analysis of Transformed Phenotypes

    • Reports

  • Smith, MT., Álvarez, MA. and Lawrence, ND., 2018. Gaussian Process Regression for Binned Data.
  • Lawrence, ND., 2010. A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
  • Lawrence, ND., 2010. A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. A Probabilistic Model to Integrate Chip and Microarray Data
  • Lawrence, ND., 2006. Large Scale Learning with the Gaussian Process Latent Variable Model
  • Lawrence, ND., 2006. The Gaussian Process Latent Variable Model
  • Lawrence, ND., 2006. Large Scale Learning with the Gaussian Process Latent Variable Model
  • Lawrence, ND., 2006. The Gaussian Process Latent Variable Model
  • Lawrence, ND., 2006. Large Scale Learning with the Gaussian Process Latent Variable Model
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. A Probabilistic Model to Integrate Chip and Microarray Data
  • Lawrence, ND., 2006. The Gaussian Process Latent Variable Model
  • Lawrence, ND., 2006. Large Scale Learning with the Gaussian Process Latent Variable Model
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. A Probabilistic Model to Integrate Chip and Microarray Data
  • Lawrence, ND., 2006. The Gaussian Process Latent Variable Model
  • Lawrence, ND., 2006. Large Scale Learning with the Gaussian Process Latent Variable Model
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. A Probabilistic Model to Integrate Chip and Microarray Data
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. A Probabilistic Model to Integrate Chip and Microarray Data
  • Lawrence, ND., 2006. Large Scale Learning with the Gaussian Process Latent Variable Model
  • King, NJ. and Lawrence, ND., 2005. Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
  • King, NJ. and Lawrence, ND., 2005. Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
  • King, NJ. and Lawrence, ND., 2005. Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
  • King, NJ. and Lawrence, ND., 2005. Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
  • na-Centeno, TP. and Lawrence, ND., 2004. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
  • Lawrence, ND., 2004. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
  • na-Centeno, TP. and Lawrence, ND., 2004. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
  • Lawrence, ND. and Sanguinetti, G., 2004. Matching Kernels through Kullback-Leibler Divergence Minimisation
  • Lawrence, ND. and Sanguinetti, G., 2004. Matching Kernels through Kullback-Leibler Divergence Minimisation
  • Lawrence, ND. and Sanguinetti, G., 2004. Matching Kernels through Kullback-Leibler Divergence Minimisation
  • Lawrence, ND. and Sanguinetti, G., 2004. Matching Kernels through Kullback-Leibler Divergence Minimisation
  • Lawrence, ND., 2004. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
  • Lawrence, ND., 2004. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
  • Lawrence, ND., 2004. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
  • Lawrence, ND., 2004. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
  • na-Centeno, TP. and Lawrence, ND., 2004. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
  • na-Centeno, TP. and Lawrence, ND., 2004. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
  • na-Centeno, TP. and Lawrence, ND., 2004. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
  • Lawrence, ND. and Tipping, ME., 2003. Generalised Component Analysis
  • Lawrence, ND. and Tipping, ME., 2003. Generalised Component Analysis
  • Lawrence, ND. and Tipping, ME., 2003. Generalised Component Analysis
  • Lawrence, ND. and Tipping, ME., 2003. Generalised Component Analysis
  • Lawrence, ND. and Tipping, ME., 2003. Generalised Component Analysis
  • Lawrence, ND., Seeger, M. and Herbrich, R., 2002. Sparse Bayesian Learning: The Informative Vector Machine
  • Lawrence, ND., Seeger, M. and Herbrich, R., 2002. Sparse Bayesian Learning: The Informative Vector Machine
  • Lawrence, ND., Seeger, M. and Herbrich, R., 2002. Sparse Bayesian Learning: The Informative Vector Machine
  • Lawrence, ND., 2002. Variational Inference Guide
  • Lawrence, ND., 2002. Variational Inference Guide
  • Lawrence, ND., 2002. Variational Inference Guide
  • Lawrence, ND., 2002. Variational Inference Guide
  • Lawrence, ND., 2002. Variational Inference Guide
  • Lawrence, ND., Seeger, M. and Herbrich, R., 2002. Sparse Bayesian Learning: The Informative Vector Machine
  • Lawrence, ND. and Azzouzi, M., 2001. The Structure of Neural Network Posteriors
  • Lawrence, ND. and Azzouzi, M., 2001. The Structure of Neural Network Posteriors
  • Lawrence, ND. and Azzouzi, M., 2001. The Structure of Neural Network Posteriors
  • Lawrence, ND. and Azzouzi, M., 2001. The Structure of Neural Network Posteriors
  • Lawrence, ND. and Bishop, CM., 2000. Variational Bayesian Independent Component Analysis
  • Lawrence, ND. and Bishop, CM., 2000. Variational Bayesian Independent Component Analysis
  • Lawrence, ND. and Bishop, CM., 2000. Variational Bayesian Independent Component Analysis
  • Lawrence, ND. and Bishop, CM., 2000. Variational Bayesian Independent Component Analysis
  • Lawrence, ND. and Bishop, CM., 2000. Variational Bayesian Independent Component Analysis
  • Lawrence, ND. and Azzouzi, M., 1999. A Variational Bayesian Committee of Neural Networks
  • Lawrence, ND. and Azzouzi, M., 1999. A Variational Bayesian Committee of Neural Networks
  • Lawrence, ND. and Azzouzi, M., 1999. A Variational Bayesian Committee of Neural Networks
  • Lawrence, ND. and Azzouzi, M., 1999. A Variational Bayesian Committee of Neural Networks
  • Frey, BJ., Lawrence, ND. and Bishop, CM., 1998. Markovian inference in belief networks
  • Frey, BJ., Lawrence, ND. and Bishop, CM., 1998. Markovian inference in belief networks
  • Frey, BJ., Lawrence, ND. and Bishop, CM., 1998. Markovian inference in belief networks
  • Frey, BJ., Lawrence, ND. and Bishop, CM., 1998. Markovian inference in belief networks
  • Frey, BJ., Lawrence, ND. and Bishop, CM., 1998. Markovian inference in belief networks

    • Theses / dissertations

  • Damianou, A., 2015. Deep Gaussian Processes and Variational Propagation of Uncertainty
  • Damianou, A., 2015. Deep Gaussian Processes and Variational Propagation of Uncertainty
  • Damianou, A., 2015. Deep Gaussian Processes and Variational Propagation of Uncertainty
  • Damianou, A., 2015. Deep Gaussian Processes and Variational Propagation of Uncertainty
  • Lawrence, ND., 2000. Variational Inference in Probabilistic Models
  • Lawrence, ND., 2000. Variational Inference in Probabilistic Models
  • Lawrence, ND., 2000. Variational Inference in Probabilistic Models
  • Lawrence, ND., 2000. Variational Inference in Probabilistic Models
  • Lawrence, ND., 2000. Variational Inference in Probabilistic Models

    • Book chapters

  • Damianou, A., Ek, CH., Boorman, L., Lawrence, ND. and Prescott, TJ., 2015. A Top-Down Approach for a Synthetic Autobiographical Memory System
    Doi: http://doi.org/10.1007/978-3-319-22979-9_28
    •
  • Damianou, A., Ek, CH., Boorman, L., Lawrence, ND. and Prescott, TJ., 2015. A Top-Down Approach for a Synthetic Autobiographical Memory System
    Doi: http://doi.org/10.1007/978-3-319-22979-9_28
    •
  • Damianou, A., Ek, CH., Boorman, L., Lawrence, ND. and Prescott, TJ., 2015. A Top-Down Approach for a Synthetic Autobiographical Memory System
    Doi: http://doi.org/10.1007/978-3-319-22979-9_28
    •
  • Damianou, A., Ek, CH., Boorman, L., Lawrence, ND. and Prescott, TJ., 2015. A Top-Down Approach for a Synthetic Autobiographical Memory System
    Doi: http://doi.org/10.1007/978-3-319-22979-9_28
    •
  • Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data.
    Doi: http://doi.org/10.1007/978-1-62703-107-3_6
    •
  • Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data.
    Doi: http://doi.org/10.1007/978-1-62703-107-3_6
    •
  • Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data.
    Doi: http://doi.org/10.1007/978-1-62703-107-3_6
    •
  • Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data.
    Doi: http://doi.org/10.1007/978-1-62703-107-3_6
    •
  • Titsias, MK., Rattray, M. and Lawrence, ND., 2011. Markov chain Monte Carlo algorithms for Gaussian processes
    Doi: http://doi.org/10.1017/CBO9780511984679.015
    •
  • Titsias, MK., Rattray, M. and Lawrence, ND., 2011. Markov chain Monte Carlo algorithms for Gaussian processes
    Doi: http://doi.org/10.1017/CBO9780511984679.015
    •
  • Titsias, MK., Rattray, M. and Lawrence, ND., 2011. Markov chain Monte Carlo algorithms for Gaussian processes
    Doi: http://doi.org/10.1017/CBO9780511984679.015
    •
  • Titsias, MK., Rattray, M. and Lawrence, ND., 2011. Markov chain Monte Carlo algorithms for Gaussian processes
    Doi: http://doi.org/10.1017/CBO9780511984679.015
    •
  • Lawrence, ND., 2010. Introduction to Learning and Inference in Computational Systems Biology
  • Lawrence, ND., 2010. Introduction to Learning and Inference in Computational Systems Biology
  • Lawrence, ND., 2010. Introduction to Learning and Inference in Computational Systems Biology
  • Lawrence, ND., 2010. Introduction to Learning and Inference in Computational Systems Biology
  • Lawrence, ND. and Rattray, M., 2010. A Brief Introduction to Bayesian Inference
  • Lawrence, ND., 2010. Introduction to Learning and Inference in Computational Systems Biology
  • Lawrence, ND., Rattray, M., Gao, P. and Titsias, MK., 2010. Gaussian Processes for Missing Species in Biochemical Systems
  • Lawrence, ND., Rattray, M., Gao, P. and Titsias, MK., 2010. Gaussian Processes for Missing Species in Biochemical Systems
  • Lawrence, ND., Rattray, M., Gao, P. and Titsias, MK., 2010. Gaussian Processes for Missing Species in Biochemical Systems
  • Lawrence, ND. and Rattray, M., 2010. A Brief Introduction to Bayesian Inference
  • Lawrence, ND., Rattray, M., Gao, P. and Titsias, MK., 2010. Gaussian Processes for Missing Species in Biochemical Systems
  • Lawrence, ND. and Rattray, M., 2010. A Brief Introduction to Bayesian Inference
  • Lawrence, ND., Rattray, M., Gao, P. and Titsias, MK., 2010. Gaussian Processes for Missing Species in Biochemical Systems
  • Lawrence, ND. and Rattray, M., 2010. A Brief Introduction to Bayesian Inference
  • Lawrence, ND. and Rattray, M., 2010. A Brief Introduction to Bayesian Inference
  • Lawrence, ND. and Jordan, MI., 2006. Gaussian Processes and the Null-Category Noise Model
  • Lawrence, ND. and Jordan, MI., 2006. Gaussian Processes and the Null-Category Noise Model
  • Lawrence, ND. and Jordan, MI., 2006. Gaussian Processes and the Null-Category Noise Model
  • King, NJ. and Lawrence, ND., 2006. Fast Variational Inference for Gaussian Process Models Through KL-Correction
    Doi: http://doi.org/10.1007/11871842_28
    •
  • Lawrence, ND. and Jordan, MI., 2006. Gaussian Processes and the Null-Category Noise Model
  • Lawrence, ND. and Jordan, MI., 2006. Gaussian Processes and the Null-Category Noise Model
  • King, NJ. and Lawrence, ND., 2006. Fast Variational Inference for Gaussian Process Models Through KL-Correction
    Doi: http://doi.org/10.1007/11871842_28
    •
  • King, NJ. and Lawrence, ND., 2006. Fast Variational Inference for Gaussian Process Models Through KL-Correction
    Doi: http://doi.org/10.1007/11871842_28
    •
  • King, NJ. and Lawrence, ND., 2006. Fast Variational Inference for Gaussian Process Models Through KL-Correction
    Doi: http://doi.org/10.1007/11871842_28
    •
  • King, NJ. and Lawrence, ND., 2006. Fast Variational Inference for Gaussian Process Models Through KL-Correction
    Doi: http://doi.org/10.1007/11871842_28
    •
  • Sanguinetti, G. and Lawrence, ND., 2006. Missing Data in Kernel PCA
    Doi: http://doi.org/10.1007/11871842_76
    •
  • Sanguinetti, G. and Lawrence, ND., 2006. Missing Data in Kernel PCA
    Doi: http://doi.org/10.1007/11871842_76
    •
  • Neil D., L. and Michael I., J., 2006. Gaussian Processes and the Null-Category Noise Model
    Doi: 10.7551/mitpress/9780262033589.003.0008
    •
  • Sanguinetti, G. and Lawrence, ND., 2006. Missing Data in Kernel PCA
    Doi: http://doi.org/10.1007/11871842_76
    •
  • Sanguinetti, G. and Lawrence, ND., 2006. Missing Data in Kernel PCA
    Doi: http://doi.org/10.1007/11871842_76
    •
  • Sanguinetti, G. and Lawrence, ND., 2006. Missing Data in Kernel PCA
    Doi: http://doi.org/10.1007/11871842_76
    •
  • Lawrence, ND., Platt, JC. and Jordan, MI., 2005. Extensions of the Informative Vector Machine
    Doi: http://doi.org/10.1007/11559887_4
    •
  • Lawrence, ND., Platt, JC. and Jordan, MI., 2005. Extensions of the Informative Vector Machine
    Doi: http://doi.org/10.1007/11559887_4
    •
  • Lawrence, ND., Platt, JC. and Jordan, MI., 2005. Extensions of the Informative Vector Machine
    Doi: http://doi.org/10.1007/11559887_4
    •
  • Lawrence, ND., Platt, JC. and Jordan, MI., 2005. Extensions of the Informative Vector Machine
    Doi: http://doi.org/10.1007/11559887_4
    •
  • Lawrence, ND., Platt, JC. and Jordan, MI., 2005. Extensions of the Informative Vector Machine
    Doi: http://doi.org/10.1007/11559887_4
    •
  • Lawrence, ND., 2002. Note Relevance Determination
    Doi: 10.1007/978-1-4471-0219-9_11
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., Ambiguity Modeling in Latent Spaces
    Doi: http://doi.org/10.1007/978-3-540-85853-9_6
    •
  • Lawrence, N., Rattray, M., Honkela, A. and Titsias, M., Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation
    Doi: http://doi.org/10.1002/9781119970606.ch19
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., Ambiguity Modeling in Latent Spaces
    Doi: http://doi.org/10.1007/978-3-540-85853-9_6
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., Ambiguity Modeling in Latent Spaces
    Doi: http://doi.org/10.1007/978-3-540-85853-9_6
    •
  • Ek, CH., Torr, PHS. and Lawrence, ND., Gaussian Process Latent Variable Models for Human Pose Estimation
    Doi: http://doi.org/10.1007/978-3-540-78155-4_12
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., Ambiguity Modeling in Latent Spaces
    Doi: http://doi.org/10.1007/978-3-540-85853-9_6
    •
  • Ek, CH., Torr, PHS. and Lawrence, ND., Gaussian Process Latent Variable Models for Human Pose Estimation
    Doi: http://doi.org/10.1007/978-3-540-78155-4_12
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., Ambiguity Modeling in Latent Spaces
    Doi: http://doi.org/10.1007/978-3-540-85853-9_6
    •
  • Ek, CH., Torr, PHS. and Lawrence, ND., Gaussian Process Latent Variable Models for Human Pose Estimation
    Doi: http://doi.org/10.1007/978-3-540-78155-4_12
    •
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., Ambiguity Modeling in Latent Spaces
    Doi: http://doi.org/10.1007/978-3-540-85853-9_6
    •
  • Lawrence, N., Rattray, M., Honkela, A. and Titsias, M., Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation
    Doi: http://doi.org/10.1002/9781119970606.ch19
    •
  • Lawrence, N., Rattray, M., Honkela, A. and Titsias, M., Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation
    Doi: http://doi.org/10.1002/9781119970606.ch19
    •

    Books

  • Lawrence, ND., Girolami, M., Rattray, M. and Sanguinetti, G., 2009. Learning and Inference in Computational Systems Biology
  • Lawrence, ND., Girolami, M., Rattray, M. and Sanguinetti, G., 2009. Learning and Inference in Computational Systems Biology
  • Lawrence, ND., Girolami, M., Rattray, M. and Sanguinetti, G., 2009. Learning and Inference in Computational Systems Biology
  • Lawrence, ND., Girolami, M., Rattray, M. and Sanguinetti, G., 2009. Learning and Inference in Computational Systems Biology
  • Lawrence, ND., Girolami, M., Rattray, M. and Sanguinetti, G., 2009. Learning and Inference in Computational Systems Biology
  • Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, ND., 2008. Dataset shift in machine learning
  • Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, ND., 2008. Dataset shift in machine learning
  • Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, ND., 2008. Dataset shift in machine learning
  • Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, ND., 2008. Dataset shift in machine learning
  • Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, ND., 2008. Dataset shift in machine learning
  • Winkler, J., Niranjan, M. and Lawrence, N., 2005. Deterministic and statistical methods in machine learning
  • Winkler, J., Niranjan, M. and Lawrence, N., 2005. Deterministic and statistical methods in machine learning
  • Winkler, J., Niranjan, M. and Lawrence, N., 2005. Deterministic and statistical methods in machine learning
  • Winkler, J., Niranjan, M. and Lawrence, N., 2005. Deterministic and statistical methods in machine learning
  • Winkler, J., Lawrence, N. and Niranjan, M., 2005. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
  • William Farebrother, R., Puntanen, S., Styan, GPH. and Werner, HJ., 1999. Preface
    Doi: http://doi.org/10.1016/s0024-3795(99)00008-7
    •
  • William Farebrother, R., Puntanen, S., Styan, GPH. and Werner, HJ., 1999. Preface
    Doi: http://doi.org/10.1016/s0024-3795(99)00008-7
    •
  • William Farebrother, R., Puntanen, S., Styan, GPH. and Werner, HJ., 1999. Preface
    Doi: http://doi.org/10.1016/s0024-3795(99)00008-7
    •

    Software

  • Lawrence, ND., MOCAP Toolbox for MATLAB

    • Contact Details

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    (01223) 7-63798
    Email: 

    ndl21 at cam dot ac dot uk

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