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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

Book chapters

  • Ficiu, B., Lawrence, ND. and Paleyes, A., 2025. Automated Discovery of Trade-Off Between Utility, Privacy and Fairness in Machine Learning Models
    Doi: http://doi.org/10.1007/978-3-031-74630-7_9
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  • Damianou, A., Ek, CH., Boorman, L., Lawrence, ND. and Prescott, TJ., 2015. A top-down approach for a synthetic autobiographical memory system
    Doi: 10.1007/978-3-319-22979-9_28
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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.
    Doi: 10.1007/978-1-62703-107-3_6
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  • Lawrence, N., Rattray, M., Honkela, A. and Titsias, M., 2011. Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation
    Doi: 10.1002/9781119970606.ch19
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  • Titsias, MK., Rattray, M. and Lawrence, ND., 2011. Markov chain Monte Carlo algorithms for Gaussian processes
    Doi: 10.1017/CBO9780511984679.015
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  • 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., 2010. Introduction to Learning and Inference in Computational Systems Biology
  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity Modeling in Latent Spaces
  • Ek, CH., Torr, PHS. and Lawrence, ND., 2008. Gaussian process latent variable models for human pose estimation
    Doi: 10.1007/978-3-540-78155-4_12
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  • Sanguinetti, G. and Lawrence, ND., 2006. Missing data in Kernel PCA
    Doi: 10.1007/11871842_76
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  • 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: 10.1007/11871842_28
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  • Neil D., L. and Michael I., J., 2006. Gaussian Processes and the Null-Category Noise Model
    Doi: 10.7551/mitpress/9780262033589.003.0008
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  • Lawrence, ND., Platt, JC. and Jordan, MI., 2005. Extensions of the informative vector machine
    Doi: 10.1007/11559887_4
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  • Lawrence, ND., 2002. Note Relevance Determination
    Doi: 10.1007/978-1-4471-0219-9_11
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    Journal articles

  • Williams, CR., Thodoroff, P., Arthern, RJ., Byrne, J., Hosking, JS., Kaiser, M., Lawrence, ND. and Kazlauskaite, I., 2025. Calculations of extreme sea level rise scenarios are strongly dependent on ice sheet model resolution Communications Earth and Environment, v. 6
    Doi: http://doi.org/10.1038/s43247-025-02010-z
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  • Robinson, D., Cabrera, C., Gordon, AD., Lawrence, ND. and Mennen, L., 2024 (Published online). Requirements are All You Need: The Final Frontier for End-User Software Engineering ACM Transactions on Software Engineering and Methodology,
    Doi: http://doi.org/10.1145/3708524
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  • Amutorine, M., Lawrence, N. and Montgomery, J., 2024. Increasing data sharing and use for social good: Lessons from Africa's data-sharing practices during the COVID-19 response Data and Policy, v. 6
    Doi: http://doi.org/10.1017/dap.2024.43
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  • Lawrence, ND. and Montgomery, J., 2024. Accelerating AI for science: open data science for science. R Soc Open Sci, v. 11
    Doi: 10.1098/rsos.231130
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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: 10.1039/d3dd00029j
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  • 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: 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: 10.3390/e25020289
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  • Ross, J., Fridman, M., Kelepouris, N., Murray, K., Krone, N., Polak, M., Rohrer, TR., Pietropoli, A., Lawrence, N. and Backeljauw, P., 2023. Factors Associated With Response to Growth Hormone in Pediatric Growth Disorders: Results of a 5-year Registry Analysis. J Endocr Soc, v. 7
    Doi: 10.1210/jendso/bvad026
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  • Paleyes, A., Urma, RG. and Lawrence, ND., 2023. Challenges in Deploying Machine Learning: A Survey of Case Studies ACM Computing Surveys, v. 55
    Doi: 10.1145/3533378
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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
  • Damianou, A., Lawrence, ND. and Ek, CH., 2021. Multi-view learning as a nonparametric nonlinear inter-battery factor analysis Journal of Machine Learning Research, v. 22
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  • Vargas, F., Thodoroff, P., Lamacraft, A. and Lawrence, N., 2021. Solving Schrödinger Bridges via Maximum Likelihood. Entropy (Basel), v. 23
    Doi: 10.3390/e23091134
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  • Diggle, PJ., Gowers, T., Kelly, F. and Lawrence, N., 2020. Decision-making with uncertainty. Signif (Oxf), v. 17
    Doi: 10.1111/1740-9713.01463
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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: 10.1109/TII.2019.2942650
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  • Delacroix, S. and Lawrence, N., 2019 (No publication date). Disturbing the ‘One Size Fits All’, Feudal Approach to Data Governance: Bottom-Up Data Trusts SSRN Electronic Journal,
    Doi: 10.2139/ssrn.3265315
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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 Journal, v. 82
    Doi: 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: 10.1111/rssb.12320
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  • Prescott, TJ., Camilleri, D., Martinez-Hernandez, U., Damianou, A. and Lawrence, ND., 2019. Memory and mental time travel in humans and social robots. Philos Trans R Soc Lond B Biol Sci, v. 374
    Doi: 10.1098/rstb.2018.0025
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  • Sarkka, S., Alvarez, MA. and Lawrence, ND., 2019. Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems IEEE Transactions on Automatic Control, v. 64
    Doi: 10.1109/TAC.2018.2874749
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  • Lawrence, ND., 2019. Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
  • 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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  • Lake, BM., Lawrence, ND. and Tenenbaum, JB., 2018. The Emergence of Organizing Structure in Conceptual Representation. Cogn Sci, v. 42 Suppl 3
    Doi: 10.1111/cogs.12580
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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: 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, U., 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 modelling resolves Th1/Tfh fate bifurcation in malaria. Sci Immunol, v. 2
    Doi: 10.1126/sciimmunol.aal2192
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  • Lawrence, ND., 2017. Living Together: Mind and Machine Intelligence
  • Lawrence, ND., 2017. Data Readiness Levels
  • Dai, Z., Iqbal, M., Lawrence, ND. and Rattray, M., 2017. Efficient inference for sparse latent variable models of transcriptional regulation. Bioinformatics, v. 33
    Doi: 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. Proc Math Phys Eng Sci, v. 473
    Doi: 10.1098/rspa.2016.0751
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  • Zwiessele, M. and Lawrence, N., 2016. Topslam: Waddington Landscape Recovery for Single Cell Experiments
    Doi: 10.1101/057778
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  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., 2016. Detecting periodicities with gaussian processes PeerJ Computer Science, v. 2016
    Doi: 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
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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: 10.1016/j.ifacol.2016.07.353
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  • Gonzalez-Hernandez, J., Daoud, Y., Styers, J., Journeycake, JM., Channabasappa, N. and Piper, HG., 2016. Central venous thrombosis in children with intestinal failure on long-term parenteral nutrition. J Pediatr Surg, v. 51
    Doi: 10.1016/j.jpedsurg.2016.02.024
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  • Hensman, J., Rattray, M. and Lawrence, ND., 2015. Fast Nonparametric Clustering of Structured Time-Series. IEEE Trans Pattern Anal Mach Intell, v. 37
    Doi: 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: 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. Proc Natl Acad Sci U S A, v. 112
    Doi: 10.1073/pnas.1420404112
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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 Comput Biol, v. 10
    Doi: 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. Nat Commun, v. 5
    Doi: 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: 10.1186/1475-2875-13-S1-P5
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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: 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: 10.1007/s10618-013-0309-y
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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 Neuropathol, v. 125
    Doi: 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: 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: 10.1093/brain/awt250
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  • Álvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear latent force models using Gaussian processes. IEEE Trans Pattern Anal Mach Intell, v. 35
    Doi: 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: 10.1093/bioinformatics/btt148
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  • Lawrence, ND., 2012. A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models Journal of Machine Learning Research, v. 13
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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 Syst Biol, v. 6
    Doi: 10.1186/1752-0509-6-53
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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 Comput Biol, v. 8
    Doi: 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 Res, v. 40
    Doi: 10.1093/nar/gkr1240
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  • Álvarez, MA., Rosasco, L. and Lawrence, ND., 2012. Kernels for Vector-Valued Functions: A Review
    Doi: 10.1561/9781601985590
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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
    Doi: 10.1109/TPAMI.2012.75
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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 Comput Biol, v. 8
    Doi: 10.1371/journal.pcbi.1002330
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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: 10.1016/j.patcog.2011.10.004
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  • Fusi, N., Stegle, O. and Lawrence, N., 2011 (Published online). Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects Nature Precedings,
    Doi: 10.1038/npre.2011.5995.1
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  • Honkela, A., Gao, P., Ropponen, J., Rattray, M. and Lawrence, ND., 2011. tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor. Bioinformatics, v. 27
    Doi: 10.1093/bioinformatics/btr057
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  • Kalaitzis, AA. and Lawrence, ND., 2011. A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression. BMC Bioinformatics, v. 12
    Doi: 10.1186/1471-2105-12-180
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  • Álvarez, MA. and Lawrence, ND., 2011. Computationally efficient convolved multiple output gaussian processes Journal of Machine Learning Research, v. 12
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  • Álvarez, MA., Rosasco, L. and Lawrence, ND., 2011. Kernels for vector-valued functions: A review Foundations and Trends in Machine Learning, v. 4
    Doi: 10.1561/2200000036
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  • Álvarez, MA. and Lawrence, ND., 2010 (Accepted for publication). Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
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  • Honkela, A., Girardot, C., Gustafson, EH., Liu, Y-H., Furlong, EEM., Lawrence, ND. and Rattray, M., 2010. Model-based method for transcription factor target identification with limited data. Proc Natl Acad Sci U S A, v. 107
    Doi: 10.1073/pnas.0914285107
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  • Asif, HMS., Rolfe, MD., Green, J., Lawrence, ND., Rattray, M. and Sanguinetti, G., 2010. TFInfer: a tool for probabilistic inference of transcription factor activities. Bioinformatics, v. 26
    Doi: 10.1093/bioinformatics/btq469
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  • Zampini, V., Johnson, SL., Franz, C., Lawrence, ND., Münkner, S., Engel, J., Knipper, M., Magistretti, J., Masetto, S. and Marcotti, W., 2010. Elementary properties of CaV1.3 Ca(2+) channels expressed in mouse cochlear inner hair cells. J Physiol, v. 588
    Doi: 10.1113/jphysiol.2009.181917
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  • Pearson, RD., Liu, X., Sanguinetti, G., Milo, M., Lawrence, ND. and Rattray, M., 2009. puma: a Bioconductor package for propagating uncertainty in microarray analysis. BMC Bioinformatics, v. 10
    Doi: 10.1186/1471-2105-10-211
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  • Gao, P., Honkela, A., Rattray, M. and Lawrence, ND., 2008. Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. Bioinformatics, v. 24
    Doi: 10.1093/bioinformatics/btn278
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  • Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, ND., 2006. Propagating uncertainty in microarray data analysis. Brief Bioinform, v. 7
    Doi: 10.1093/bib/bbk003
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  • Liu, X., Milo, M., Lawrence, ND. and Rattray, M., 2006. Probe-level measurement error improves accuracy in detecting differential gene expression. Bioinformatics, v. 22
    Doi: 10.1093/bioinformatics/btl361
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  • Centeno, TP. and Lawrence, ND., 2006. Optimising kernel parameters and regularisation coefficients for non-linear discriminant analysis Journal of Machine Learning Research, v. 7
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  • Sanguinetti, G., Lawrence, ND. and Rattray, M., 2006. Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinformatics, v. 22
    Doi: 10.1093/bioinformatics/btl473
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  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinformatics, v. 22
    Doi: 10.1093/bioinformatics/btl154
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  • Sanguinetti, G., Milo, M., Rattray, M. and Lawrence, ND., 2005. Accounting for probe-level noise in principal component analysis of microarray data. Bioinformatics, v. 21
    Doi: 10.1093/bioinformatics/bti617
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  • Liu, X., Milo, M., Lawrence, ND. and Rattray, M., 2005. A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips. Bioinformatics, v. 21
    Doi: 10.1093/bioinformatics/bti583
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  • Lawrence, N., 2005. Probabilistic non-linear principal component analysis with Gaussian process latent variable models Journal of Machine Learning Research, v. 6
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  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., 2004. Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics, v. 20
    Doi: 10.1093/bioinformatics/btg438
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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. Biochem Soc Trans, v. 31
    Doi: 10.1042/bst0311510
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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
    Doi: 10.1007/s005210170016
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    Conference proceedings

  • Cabrera, C., Paleyes, A. and Lawrence, ND., 2024. Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation Proceedings - 2024 IEEE/ACM International Workshop New Trends in Software Architecture, SATrends 2024,
    Doi: 10.1145/3643657.3643910
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  • Paleyes, A., Li, HB. and Lawrence, ND., 2024. Can causality accelerate experimentation in software systems? Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024,
    Doi: 10.1145/3644815.3644985
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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: 10.1109/CAIN58948.2023.00010
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  • Paleyes, A. and Lawrence, ND., 2023. Causal fault localisation in dataflow systems Proceedings of the 3rd Workshop on Machine Learning and Systems,
    Doi: 10.1145/3578356.3592593
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  • Paleyes, A., Mahsereci, M. and Lawrence, N., 2023. Emukit: A Python toolkit for decision making under uncertainty Proceedings of the Python in Science Conference,
    Doi: 10.25080/gerudo-f2bc6f59-009
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  • Bell, SJ., Kampman, OP., Dodge, J. and Lawrence, ND., 2022. Modeling the Machine Learning Multiverse Advances in Neural Information Processing Systems, v. 35
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  • 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
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  • Thodoroff, P., Li, W. and Lawrence, ND., 2022. Benchmarking Real-Time Reinforcement Learning Proceedings of Machine Learning Research, v. 181
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  • 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: 10.1145/3522664.3528601
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  • 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
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  • 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
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  • 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,
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  • 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
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  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
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  • Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
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  • 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
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  • 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,
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  • 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
    •
  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient modeling of latent information in supervised learning using Gaussian processes Advances in Neural Information Processing Systems, v. 2017-December
    •
  • Grigorievskiy, A., Lawrence, N. and Sarkka, S., 2017. Parallelizable sparse inverse formulation Gaussian processes (SpInGP) IEEE International Workshop on Machine Learning for Signal Processing, MLSP, v. 2017-September
    Doi: 10.1109/MLSP.2017.8168130
    •
  • Gonzalez, J., Dai, Z., Damianou, A. and Lawrence, ND., 2017. Preferential Bayesian optimization 34th International Conference on Machine Learning, ICML 2017, v. 3
    •
  • 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 2016,
    Doi: 10.1109/ROBIO.2016.7866589
    •
  • Rahman, MA. and Lawrence, ND., 2016. A Gaussian process model for inferring the dynamic transcription factor activity ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics,
    Doi: 10.1145/2975167.2985651
    •
  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2016. Monitoring short term changes of infectious diseases in Uganda with gaussian processes Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9785 LNCS
    Doi: 10.1007/978-3-319-44412-3_7
    •
  • 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., 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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9793
    Doi: 10.1007/978-3-319-42417-0_5
    •
  • 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,
    •
  • 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,
    •
  • 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,
    •
  • 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
    •
  • 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,
    •
  • Welling, M., Ghahramani, Z., Cortes, C., Lawrence, N. and Weinberger, K., 2014. Preface Advances in Neural Information Processing Systems, v. 1
    •
  • 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,
    •
  • 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,
    •
  • 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
    •
  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
    •
  • 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
    •
  • Kalaitzis, AA. and Lawrence, ND., 2011. Residual Component Analysis
  • 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,
    •
  • 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,
    •
  • 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., Luengo, D., Titsias, MK. and Lawrence, ND., 2010 (Published online). Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 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
    •
  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010,
    Doi: 10.1109/MLSP.2010.5589258
    •
  • 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,
    •
  • 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,
    •
  • 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 ACM International Conference Proceeding Series, v. 382
    Doi: 10.1145/1553374.1553452
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND. and Melhuish, C., 2009. Shared gaussian process latent variable models for handling ambiguous facial expressions AIP Conference Proceedings, v. 1107
    Doi: 10.1063/1.3106464
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: 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 British Machine Vision Conference, BMVC 2009 - Proceedings,
    Doi: 10.5244/C.23.11
    •
  • 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,
    •
  • 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,
    •
  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-constrained latent variable models Proceedings of the 25th International Conference on Machine Learning,
    Doi: 10.1145/1390156.1390292
    •
  • 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: 10.1007/978-3-540-85853-9_6
    •
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., 2007. Modeling human locomotion with topologically constrained latent variable models Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 4814 LNCS
    Doi: 10.1007/978-3-540-75703-0_8
    •
  • 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. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ACM International Conference Proceeding Series, v. 227
    Doi: 10.1145/1273496.1273557
    •
  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
    •
  • 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
    •
  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian process latent variable models for fault detection Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007,
    Doi: 10.1109/CIDM.2007.368886
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ACM International Conference Proceeding Series, v. 148
    Doi: 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
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying submodules of cellular regulatory networks Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 4210 LNBI
    Doi: 10.1007/11885191_11
    •
  • 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,
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., 2005. Automatic determination of the number of clusters using spectral algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: 10.1109/MLSP.2005.1532874
    •
  • 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 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: 10.1016/j.neucom.2005.02.016
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Abdel-Haleem, YH., Renals, S. and Lawrence, ND., 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 5
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to Learn with the Informative Vector Machine Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2003 (Accepted for publication). Gaussian Process Models for Visualisation of High Dimensional Data
  • Seeger, M., Williams, CKI. and Lawrence, ND., 2003 (Accepted for publication). Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Tipping, ME. and Lawrence, ND., 2003. A variational approach to robust Bayesian interpolation Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, v. 2003-January
    Doi: 10.1109/NNSP.2003.1318022
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., 2003. Bayesian processing of microarray images Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, v. 2003-January
    Doi: 10.1109/NNSP.2003.1318005
    •
  • Lawrence, N., Seeger, M. and Herbrich, R., 2003. Fast sparse Gaussian process methods: The informative vector machine Advances in Neural Information Processing Systems,
    •
  • Vermaak, J., Lawrence, ND. and Pérez, P., 2003. Variational inference for visual tracking Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 1
    •
  • 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. and Schölkopf, B., 2001 (Accepted for publication). Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., 2001. Probabilistic modelling of replica divergence Proceedings of the Workshop on Hot Topics in Operating Systems - HOTOS,
    •
  • Lawrence, ND., 2000 (Accepted for publication). Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • 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,
    •

    Other publications

  • Lawrence, ND., 2021. Machine Learning and the Physical World
    Doi: 10.52843/cassyni.qzyrlv
    •
  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., 2019 (No publication date). Gaussian process models for periodicity detection
  • Hensman, J. and Lawrence, ND., 2019 (No publication date). Nested Variational Compression in Deep Gaussian Processes
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2019 (No publication date). Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
  • Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2019 (No publication date). Genetic Analysis of Transformed Phenotypes
  • Smith, MT., Zwiessele, M. and Lawrence, ND., 2019 (No publication date). Differentially Private Gaussian Processes
  • 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: 10.1126/sciimmunol.aat1469
    •
  • Lawrence, ND., 2010. A Probabilistic Perspective on Spectral Dimensionality Reduction
  • Lawrence, ND., 2007. Variational Optimisation by Marginal Matching
  • Lawrence, ND. and Milo, M., 2003. Variational Importance Sampling
  • Lawrence, ND., 2003. Particle Filters, Variational methods and Importance Sampling

    • Internet publications

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

    • Software

  • Lawrence, ND., 2019 (No publication date). MOCAP Toolbox for MATLAB

    • 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., 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
  • King, NJ. and Lawrence, ND., 2005. Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
  • 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 Tipping, ME., 2003. Generalised Component Analysis
  • 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 Bishop, CM., 2000. Variational Bayesian Independent Component Analysis
  • 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

    • Books

  • Rahimi-Movaghar, V., Jazayeri, SB. and Vaccaro, AR., 2012. Preface
  • Lawrence, ND., Girolami, M., Rattray, M. and Sanguinetti, G., 2009. Learning and Inference in Computational Systems Biology
  • 2008. Dataset Shift in Machine Learning
    Doi: 10.7551/mitpress/9780262170055.001.0001
    •
  • 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
  • 2005. Deterministic and Statistical Methods in Machine Learning
    Doi: 10.1007/11559887
    •

    Theses / dissertations

  • Lawrence, ND., 2000. Variational Inference in Probabilistic Models

    • Contact Details

    Room: 
    FE05
    Office phone: 
    (01223) 7-63798
    Email: 

    ndl21 at cam dot ac dot uk

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