Books

  • Quiñonero-Candela J, Sugiyama M, Schwaighofer A & Lawrence ND (2008) Dataset shift in machine learning. The MIT Press.
  • 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.
  • Winkler J, Niranjan M & Lawrence N (2005) Deterministic and statistical methods in machine learning. Springer Verlag.

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
  • 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
  • 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
  • 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
  • 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

Conference proceedings papers

  • 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
  • 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)
  • 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)
  • 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)
  • 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
  • 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)
  • 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)
  • 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
  • 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)
  • Hensman J, Zwießele M & Lawrence ND (2014) Tilted variational bayes. Journal of Machine Learning Research, Vol. 33 (pp 356-364)
  • 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)
  • Welling M, Ghahramani Z, Cortes C, Lawrence N & Weinberger K (2014) Preface. Advances in Neural Information Processing Systems, Vol. 1(January) (pp xxxi-xxxiv)
  • 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)
  • 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)
  • Damianou AC, Titsias MK & Lawrence ND (2011) Variational Gaussian Process Dynamical Systems.. NIPS (pp 2510-2518)
  • Titsias MK & Lawrence ND (2010) Bayesian Gaussian Process Latent Variable Model, Vol. 9 (pp 844-851)
  • Álvarez MA, Luengo D, Titsias MK & Lawrence ND (2010) Efficient Multioutput Gaussian Processes through Variational Inducing Kernels, Vol. 9 (pp 25-32)
  • 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)
  • Álvarez M, Luengo D & Lawrence ND (2009) Latent Force Models (pp 9-16)
  • 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
  • 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)
  • Lawrence ND & Urtasun R (2009) Non-linear matrix factorization with gaussian processes. ACM International Conference Proceeding Series, Vol. 382
  • 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)
  • Urtasun R, Fleet DJ, Geiger A, Popović J, Darrell TJ & Lawrence ND (2008) Topologically-Constrained Latent Variable Models (pp 1080-1087-1080-1087)
  • 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)
  • 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)
  • 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)
  • Lawrence ND, Sanguinetti G & Rattray M (2007) Modelling transcriptional regulation using Gaussian Processes. Neural Information Processing Systems, Vol. 19 (pp 785-792). Vancouver
  • 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)
  • 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)
  • 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)
  • 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)
  • Lawrence ND & Moore AJ (2007) Hierarchical Gaussian process latent variable models. ACM International Conference Proceeding Series, Vol. 227 (pp 481-488)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • Sanguinetti G, Rattray M & Lawrence ND (2006) Identifying submodules of cellular regulatory networks. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY, PROCEEDINGS, Vol. 4210 (pp 155-168)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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
  • 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)
  • 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)
  • 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
  • Lawrence ND, Seeger MW & Herbrich R (2002) Fast Sparse Gaussian Process Methods: The Informative Vector Machine.. NIPS (pp 609-616)
  • 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)
  • 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)
  • Lawrence ND, Bishop CM & Jordan MI (1998) Mixture Representations for Inference and Learning in Boltzmann Machines (pp 320-327)
  • Álvarez MA, Peters J, Schölkopf B & Lawrence ND () Switched Latent Force Models for Movement Segmentation
  • Titsias MK, Lawrence ND & Rattray M () Efficient Sampling for Gaussian Process Inference using Control Variables (pp 1681-1688)
  • Calderhead B, Girolami M & Lawrence ND () Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes (pp 217-224)
  • Álvarez M & Lawrence ND () Sparse Convolved Gaussian Processes for Multi-output Regression (pp 57-64)
  • Lawrence ND () Learning for Larger Datasets with the Gaussian Process Latent Variable Model (pp 243-250)
  • Lawrence ND & Jordan MI () Semi-supervised Learning via Gaussian Processes (pp 753-760)
  • Tipping ME & Lawrence ND () A Variational Approach to Robust Bayesian Interpolation (pp 229-238)
  • Seeger M, Williams CKI & Lawrence ND () Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Lawrence ND, Seeger M & Herbrich R () Fast Sparse Gaussian Process Methods: The Informative Vector Machine (pp 625-632)
  • Lawrence ND () Gaussian Process Models for Visualisation of High Dimensional Data (pp 329-336)
  • Lawrence ND () Node Relevance Determination
  • Lawrence ND & Schölkopf B () Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Lawrence ND () Variational Learning for Multi-layer networks of Linear Threshold Units (pp 245-252)
  • Bishop CM, Lawrence ND, Jaakkola TS & Jordan MI () Approximating Posterior Distributions in Belief Networks using Mixtures (pp 416-422)

Reports

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

Other

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

Theses / Dissertations

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

Edited books

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