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: 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: 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: 10.1017/CBO9780511984679.015
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
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
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
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
Lawrence, ND., Platt, JC. and Jordan, MI., 2005. Extensions of the informative vector machine
Doi: 10.1007/11559887_4
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
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
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
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
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
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
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
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
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
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
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,
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
Vargas, F., Thodoroff, P., Lamacraft, A. and Lawrence, N., 2021. Solving Schrödinger Bridges via Maximum Likelihood. Entropy (Basel), v. 23
Doi: 10.3390/e23091134
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
Diggle, PJ., Gowers, T., Kelly, F. and Lawrence, N., 2020. Decision-making with uncertainty. Signif (Oxf), v. 17
Doi: 10.1111/1740-9713.01463
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
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
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
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
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
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,
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
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
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
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
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
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
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
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
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
Zwiessele, M. and Lawrence, N., 2016. Topslam: Waddington Landscape Recovery for Single Cell Experiments
Doi: 10.1101/057778
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Álvarez, MA., Rosasco, L. and Lawrence, ND., 2012. Kernels for Vector-Valued Functions: A Review
Doi: 10.1561/9781601985590
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
Lawrence, ND., 2012. A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models Journal of Machine Learning Research, v. 13
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
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
Álvarez, MA. and Lawrence, ND., 2011. Computationally efficient convolved multiple output gaussian processes Journal of Machine Learning Research, v. 12
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
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
Álvarez, MA. and Lawrence, ND., 2010 (Accepted for publication). Computationally Efficient Convolved Multiple Output Gaussian
Processes Journal of Machine Learning Research, v. 12 (2011)
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
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
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
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
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
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
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
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
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
Centeno, TP. and Lawrence, ND., 2006. Optimising kernel parameters and regularisation coefficients for non-linear discriminant analysis Journal of Machine Learning Research, v. 7
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
Lawrence, N., 2005. Probabilistic non-linear principal component analysis with Gaussian process latent variable models Journal of Machine Learning Research, v. 6
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
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
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
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
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
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
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
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
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
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: 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
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
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,
Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
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,
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
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,
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
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
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,
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,
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,
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
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
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
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
Welling, M., Ghahramani, Z., Cortes, C., Lawrence, N. and Weinberger, K., 2014. Preface Advances in Neural Information Processing Systems, v. 1
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,
Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 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
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
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, 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,
Kalaitzis, AA. and Lawrence, ND., 2011. Residual Component Analysis
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,
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,
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., 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. 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,
Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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., 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
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, 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, 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. 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
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
Hensman, J. and Lawrence, ND., 2019 (No publication date). Nested Variational Compression in Deep Gaussian Processes
Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., 2019 (No publication date). Gaussian process models for periodicity detection
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., 2003. Particle Filters, Variational methods and Importance Sampling
Lawrence, ND. and Milo, M., 2003. Variational 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. 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
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
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
