Books

  • Quiñonero-Candela J, Sugiyama M, Schwaighofer A & Lawrence ND (2008) Dataset shift in machine learning. The MIT Press. RIS download Bibtex download
  • Winkler J, Lawrence N & Niranjan M (2005) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. RIS download Bibtex download
  • Winkler J, Niranjan M & Lawrence N (2005) Deterministic and statistical methods in machine learning. Springer Verlag. RIS download Bibtex download

Journal articles

Chapters

  • Titsias MK, Rattray M & Lawrence ND (2011) Markov chain Monte Carlo algorithms for Gaussian processes In Barber D, Taylan Cemgil A & Chiappa S (Ed.), Bayesian Time Series Models Cambridge University Press RIS download Bibtex download
  • Lawrence ND & Rattray M (2010) A Brief Introduction to Bayesian Inference In Lawrence ND, Girolami M, Rattray M & Sanguinetti G (Ed.), Learning And Inference In Computational Systems Biology (pp. 97-116). MIT Press RIS download Bibtex download
  • Lawrence ND, Rattray M, Gao P & Titsias MK (2010) Gaussian Processes for Missing Species in Biochemical Systems In Lawrence ND, Girolami M, Rattray M & Sanguinetti G (Ed.), Learning And Inference In Computational Systems Biology (pp. 231-252). MIT Press RIS download Bibtex download
  • Lawrence ND (2010) Introduction to Learning and Inference in Computational Systems Biology In Lawrence ND, Girolami M, Rattray M & Sanguinetti G (Ed.), Learning and Inference in Computational Systems Biology (pp. 1-8). MIT Press RIS download Bibtex download
  • Lawrence ND & Jordan MI (2006) Gaussian Processes and the Null-Category Noise Model In Chapelle O, Schölkopf B & Zien A (Ed.), Semi-Supervised Learning (pp. 137-150). MIT Press RIS download Bibtex download

Conference proceedings papers

  • Grigorievskiy A, Lawrence N & Sarkka S (2017) Parallelizable sparse inverse formulation Gaussian processes (SpInGP). IEEE International Workshop on Machine Learning for Signal Processing, MLSP, Vol. 2017-September (pp 1-6) RIS download Bibtex download
  • Alvarez Lopez MA (2017) Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. Proceedings of the Neural Information Processing Systems Conference View this article in WRRO RIS download Bibtex download
  • Gonzalez J, Dai Z, Damianou A & Lawrence ND (2017) Preferential Bayesian optimization. 34th International Conference on Machine Learning, ICML 2017, Vol. 3 (pp 2080-2089) RIS download Bibtex download
  • Martinez-Hernandez U, Damianou A, Camilleri D, Boorman LW, Lawrence N & Prescott AJ (2016) An integrated probabilistic framework for robot perception, learning and memory. 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), 3 December 2016 - 7 December 2016. View this article in WRRO RIS download Bibtex download
  • Rahman MA & 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 (pp 495-496) RIS download Bibtex download
  • Mattos CLC, Damianou A, Barreto GA & Lawrence ND (2016) Latent Autoregressive Gaussian Processes Models for Robust System Identification. IFAC-PapersOnLine, Vol. 49(7) (pp 1121-1126) RIS download Bibtex download
  • Andrade-Pacheco R, Mubangizi M, Quinn J & 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), Vol. 9785 LNCS (pp 95-110) RIS download Bibtex download
  • Camilleri D, Damianou A, Jackson H, Lawrence N & 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), Vol. 9793 (pp 48-57) View this article in WRRO RIS download Bibtex download
  • (2015) Semi-described and semi-supervised learning with Gaussian processes. Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015 (pp 228-237) View this article in WRRO RIS download Bibtex download
  • Damianou A & Lawrence ND (2015) Semi-described and semi-supervised learning with Gaussian processes. Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015 (pp 228-237) RIS download Bibtex download
  • Vanschoren J, Bischl B, Hutter F, Sebag M, Kegl B, Schmid M, Napolitano G, Wolstencroft K, Williams AR & 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), Vol. 9385 (pp XIX-XXI) RIS download Bibtex download
  • Damianou A, Ek CH, Boorman L, Lawrence ND & Prescott TJ (2015) A top-down approach for a synthetic autobiographical memory system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9222 (pp 280-292) View this article in WRRO RIS download Bibtex download
  • Andrade-Pacheco R, Mubangizi M, Quinn J & Lawrence N (2015) Monitoring short term changes of malaria incidence in Uganda with Gaussian processes. CEUR Workshop Proceedings, Vol. 1425 (pp 3-9) RIS download Bibtex download
  • Hensman J, Zwießele M & Lawrence ND (2014) Tilted variational bayes. Journal of Machine Learning Research, Vol. 33 (pp 356-364) RIS download Bibtex download
  • Andrade-Pacheco R, Hensman J, Zwießele M & Lawrence ND (2014) Hybrid discriminative-generative approach with Gaussian processes. Journal of Machine Learning Research, Vol. 33 (pp 47-56) RIS download Bibtex download
  • Welling M, Ghahramani Z, Cortes C, Lawrence N & Weinberger K (2014) Preface. Advances in Neural Information Processing Systems, Vol. 1(January) (pp xxxi-xxxiv) RIS download Bibtex download
  • Tosi A, Hauberg S, Vellido A & Lawrence ND (2014) Metrics for probabilistic geometries. Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 (pp 800-808) RIS download Bibtex download
  • Maxwell JR, Taylor LH, Pachecho RA, Lawrence N, Duff GW, Teare MD & 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, Vol. 64(10) (pp S427-S427) RIS download Bibtex download
  • Damianou AC, Titsias MK & Lawrence ND (2011) Variational Gaussian Process Dynamical Systems.. NIPS (pp 2510-2518) RIS download Bibtex download
  • Titsias MK & Lawrence ND (2010) Bayesian Gaussian Process Latent Variable Model, Vol. 9 (pp 844-851) RIS download Bibtex download
  • Álvarez MA, Luengo D, Titsias MK & Lawrence ND (2010) Efficient Multioutput Gaussian Processes through Variational Inducing Kernels, Vol. 9 (pp 25-32) RIS download Bibtex download
  • Honkela A, Milo M, Holley M, Rattray M & 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 (pp 154-159) RIS download Bibtex download
  • Álvarez M, Luengo D & Lawrence ND (2009) Latent Force Models (pp 9-16) RIS download Bibtex download
  • Darby J, Li B, Costen N, Fleet DJ & Lawrence ND (2009) Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery. British Machine Vision Conference RIS download Bibtex download
  • Ek CH, Jaeckel P, Campbell N, Lawrence ND & Melhuish C (2009) Shared gaussian process latent variable models for handling ambiguous facial expressions. AIP Conference Proceedings, Vol. 1107 (pp 147-153) RIS download Bibtex download
  • Lawrence ND & Urtasun R (2009) Non-linear matrix factorization with gaussian processes. ACM International Conference Proceeding Series, Vol. 382 RIS download Bibtex download
  • Lawrence ND & Urtasun R (2009) Non-linear matrix factorization with Gaussian processes. Proceedings of the 26th International Conference On Machine Learning, ICML 2009 (pp 601-608) RIS download Bibtex download
  • Urtasun R, Fleet DJ, Geiger A, Popović J, Darrell TJ & Lawrence ND (2008) Topologically-Constrained Latent Variable Models (pp 1080-1087-1080-1087) RIS download Bibtex download
  • Ek CH, Torr PHS & Lawrence ND (2008) Gaussian process latent variable models for human pose estimation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4892 LNCS (pp 132-143) RIS download Bibtex download
  • Urtasun R, Fleet DJ, Geiger A, Popović J, Darrell TJ & Lawrence ND (2008) Topologically-constrained latent variable models. Proceedings of the 25th International Conference on Machine Learning (pp 1080-1087) RIS download Bibtex download
  • Ek CH, Rihan J, Torr PHS, Rogez G & 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), Vol. 5237 LNCS (pp 62-73) RIS download Bibtex download
  • Lawrence ND, Sanguinetti G & Rattray M (2007) Modelling transcriptional regulation using Gaussian Processes. Neural Information Processing Systems, Vol. 19 (pp 785-792). Vancouver RIS download Bibtex download
  • Ferris BD, Fox D & Lawrence ND (2007) WiFi-SLAM Using Gaussian Process Latent Variable Models. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007) (pp 2480-2485) RIS download Bibtex download
  • Urtasun R, Fleet DJ & 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), Vol. 4814 LNCS (pp 104-118) RIS download Bibtex download
  • Laidler J, Cooke M & 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, Vol. 3 (pp 1677-1680) RIS download Bibtex download
  • Lawrence ND & Moore AJ (2007) Hierarchical Gaussian process latent variable models. ICML 2007 - Proceedings of the 24th International Conference on Machine Learning (pp 481-488) RIS download Bibtex download
  • Lawrence ND & Moore AJ (2007) Hierarchical Gaussian process latent variable models. ACM International Conference Proceeding Series, Vol. 227 (pp 481-488) RIS download Bibtex download
  • Eciolaza L, Alkarouri A, Lawrence ND, Kadirkamanathan V & Fleming PJ (2007) Gaussian process latent variable models for fault detection. 2007 IEEE Symposium on Computational Intelligence and Data Mining, Vols 1 and 2 (pp 287-292) RIS download Bibtex download
  • Lawrence ND & 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, Vol. 2006 (pp 513-520) RIS download Bibtex download
  • Sanguinetti G & Lawrence ND (2006) Missing data in Kernel PCA. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4212 LNAI (pp 751-758) RIS download Bibtex download
  • King NJ & Lawrence ND (2006) Fast variational inference for Gaussian process models through KL-correction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4212 LNAI (pp 270-281) RIS download Bibtex download
  • Lawrence ND & Quiñonero-Candela J (2006) Local distance preservation in the GP-LVM through back constraints. ACM International Conference Proceeding Series, Vol. 148 (pp 513-520) RIS download Bibtex download
  • Sanguinetti G, Rattray M & Lawrence ND (2006) Identifying submodules of cellular regulatory networks. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, Vol. 4210 (pp 155-168) RIS download Bibtex download
  • Sanguinetti G, Laidler J & Lawrence ND (2005) Automatic determination of the number of clusters using spectral algorithms. 2005 IEEE Workshop on Machine Learning for Signal Processing (pp 55-60) RIS download Bibtex download
  • Hifny Y, Renais S & Lawrence ND (2005) A hybrid MaxEnt/HMM based ASR system. 9th European Conference on Speech Communication and Technology (pp 3017-3020) RIS download Bibtex download
  • Lawrence ND, Platt JC & Jordan MI (2005) Extensions of the informative vector machine. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3635 LNAI (pp 56-87) RIS download Bibtex download
  • Abdel-Haleem YH, Renals S & Lawrence ND (2004) Acoustic space dimensionality selection and combination using the maximum entropy principle. 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS (pp 637-640) RIS download Bibtex download
  • Lawrence ND (2004) Gaussian process latent variable models for visualisation of high dimensional data. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16, Vol. 16 (pp 329-336) RIS download Bibtex download
  • Abdel-Haleem YH, Renals S & 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, Vol. 5 RIS download Bibtex download
  • Lawrence ND & Platt JC (2004) Learning to Learn with the Informative Vector Machine. Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 (pp 512-519) RIS download Bibtex download
  • Lawrence ND, Milo M, Niranjan M, Rashbass P & Soullier S (2003) Bayesian processing of microarray images. 2003 IEEE XIII WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING - NNSP'03 (pp 71-80) RIS download Bibtex download
  • Vermaak J, Lawrence ND & Pérez P (2003) Variational inference for visual tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1 RIS download Bibtex download
  • Lawrence ND, Seeger MW & Herbrich R (2002) Fast Sparse Gaussian Process Methods: The Informative Vector Machine.. NIPS (pp 609-616) RIS download Bibtex download
  • Lawrence ND, Rowstron AIT, Bishop CM & Taylor MJ (2002) Optimising synchronisation times for mobile devices. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14, VOLS 1 AND 2, Vol. 14 (pp 1401-1408) RIS download Bibtex download
  • Rowstron AIT, Lawrence ND & Bishop CM (2001) Probabilistic Modelling of Replica Divergence. Proceedings of the 8th Workshop on Hot Topics in Operating Systems HOTOS (VIII) RIS download Bibtex download
  • Lawrence ND, Bishop CM & Jordan MI (1998) Mixture Representations for Inference and Learning in Boltzmann Machines (pp 320-327) RIS download Bibtex download
  • Álvarez MA, Peters J, Schölkopf B & Lawrence ND () Switched Latent Force Models for Movement Segmentation RIS download Bibtex download
  • Titsias MK, Lawrence ND & Rattray M () Efficient Sampling for Gaussian Process Inference using Control Variables (pp 1681-1688) RIS download Bibtex download
  • Calderhead B, Girolami M & Lawrence ND () Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes (pp 217-224) RIS download Bibtex download
  • Álvarez M & Lawrence ND () Sparse Convolved Gaussian Processes for Multi-output Regression (pp 57-64) RIS download Bibtex download
  • Lawrence ND () Learning for Larger Datasets with the Gaussian Process Latent Variable Model (pp 243-250) RIS download Bibtex download
  • Lawrence ND & Jordan MI () Semi-supervised Learning via Gaussian Processes (pp 753-760) RIS download Bibtex download
  • Tipping ME & Lawrence ND () A Variational Approach to Robust Bayesian Interpolation (pp 229-238) RIS download Bibtex download
  • Seeger M, Williams CKI & Lawrence ND () Fast Forward Selection to Speed Up Sparse Gaussian Process Regression RIS download Bibtex download
  • Lawrence ND, Seeger M & Herbrich R () Fast Sparse Gaussian Process Methods: The Informative Vector Machine (pp 625-632) RIS download Bibtex download
  • Lawrence ND () Gaussian Process Models for Visualisation of High Dimensional Data (pp 329-336) RIS download Bibtex download
  • Lawrence ND () Node Relevance Determination RIS download Bibtex download
  • Lawrence ND & Schölkopf B () Estimating a Kernel Fisher Discriminant in the Presence of Label Noise RIS download Bibtex download
  • Lawrence ND () Variational Learning for Multi-layer networks of Linear Threshold Units (pp 245-252) RIS download Bibtex download
  • Bishop CM, Lawrence ND, Jaakkola TS & Jordan MI () Approximating Posterior Distributions in Belief Networks using Mixtures (pp 416-422) RIS download Bibtex download
  • Smith M, Alvarez Lopez MA, Zwiessele M & Lawrence N () Differentially Private Regression with Gaussian processes. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, 9 - 11 April 2018, Playa Blanca, Lanzarote, Canary Islands View this article in WRRO RIS download Bibtex download

Reports

  • Lawrence ND (2010) A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction RIS download Bibtex download
  • Álvarez MA & Lawrence ND (2009) Sparse Convolved Multiple Output Gaussian Processes RIS download Bibtex download
  • Álvarez MA, Luengo D, Titsias MK & Lawrence ND (2009) Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes RIS download Bibtex download
  • Lawrence ND (2006) The Gaussian Process Latent Variable Model RIS download Bibtex download
  • Lawrence ND (2006) Large Scale Learning with the Gaussian Process Latent Variable Model RIS download Bibtex download
  • Sanguinetti G, Rattray M & Lawrence ND (2006) A Probabilistic Model to Integrate Chip and Microarray Data RIS download Bibtex download
  • King NJ & Lawrence ND (2005) Variational Inference in Gaussian Processes via Probabilistic Point Assimilation RIS download Bibtex download
  • Lawrence ND & Sanguinetti G (2004) Matching Kernels through Kullback-Leibler Divergence Minimisation RIS download Bibtex download
  • Lawrence ND (2004) Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models RIS download Bibtex download
  • na-Centeno TP & Lawrence ND (2004) Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis RIS download Bibtex download
  • Lawrence ND & Tipping ME (2003) Generalised Component Analysis RIS download Bibtex download
  • Lawrence ND (2002) Variational Inference Guide RIS download Bibtex download
  • Lawrence ND, Seeger M & Herbrich R (2002) Sparse Bayesian Learning: The Informative Vector Machine RIS download Bibtex download
  • Lawrence ND & Azzouzi M (2001) The Structure of Neural Network Posteriors RIS download Bibtex download
  • Lawrence ND & Bishop CM (2000) Variational Bayesian Independent Component Analysis RIS download Bibtex download
  • Lawrence ND & Azzouzi M (1999) A Variational Bayesian Committee of Neural Networks RIS download Bibtex download
  • Frey BJ, Lawrence ND & Bishop CM (1998) Markovian inference in belief networks RIS download Bibtex download

Software / Code

  • Lawrence ND MOCAP Toolbox for MATLAB. RIS download Bibtex download

Other

  • Lawrence ND (2010) A Probabilistic Perspective on Spectral Dimensionality Reduction. RIS download Bibtex download
  • Lawrence ND (2007) Variational Optimisation by Marginal Matching. RIS download Bibtex download
  • Lawrence ND, Rowstron AIT, Bishop CM & Taylor MJ (2005) System and Method for Replicating Data in a Distributed System. RIS download Bibtex download
  • Lawrence ND (2003) Particle Filters, Variational methods and Importance Sampling. RIS download Bibtex download
  • Lawrence ND & Milo M (2003) Variational Importance Sampling. RIS download Bibtex download

Theses / Dissertations

  • Damianou A (2015) Deep Gaussian Processes and Variational Propagation of Uncertainty. RIS download Bibtex download
  • Lawrence ND (2000) Variational Inference in Probabilistic Models. RIS download Bibtex download

Edited books

  • Lawrence ND, Girolami M, Rattray M & Sanguinetti G (Eds.) (2009) Learning and Inference in Computational Systems Biology. The MIT Press. RIS download Bibtex download

Working papers

  • Mattos CLC, Dai Z, Damianou A, Forth J, Barreto GA & Lawrence ND () Recurrent Gaussian Processes. View this article in WRRO RIS download Bibtex download
  • Dai Z, Damianou A, González J & Lawrence N () Variational Auto-encoded Deep Gaussian Processes. RIS download Bibtex download
  • Hensman J & Lawrence ND () Nested Variational Compression in Deep Gaussian Processes. View this article in WRRO RIS download Bibtex download
  • Damianou AC, Titsias MK & Lawrence ND () Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. RIS download Bibtex download
  • Fusi N, Lippert C, Lawrence ND & Stegle O () Genetic Analysis of Transformed Phenotypes. RIS download Bibtex download
  • Durrande N, Hensman J, Rattray M & Lawrence ND () Gaussian process models for periodicity detection. RIS download Bibtex download
  • Smith MT, Zwiessele M & Lawrence ND () Differentially Private Gaussian Processes. View this article in WRRO RIS download Bibtex download
  • Damianou A, Lawrence ND & Ek CH () Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. RIS download Bibtex download
  • Lawrence ND () Living Together: Mind and Machine Intelligence. RIS download Bibtex download
  • Lawrence ND () Data Readiness Levels. RIS download Bibtex download