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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

Conference proceedings

  • Paleyes, A., Guo, S., Schölkopf, B. and Lawrence, ND., 2023. Dataflow graphs as complete causal graphs Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023,
    Doi: http://doi.org/10.1109/CAIN58948.2023.00010
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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: http://doi.org/10.1145/3578356.3592593
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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: http://doi.org/10.1145/3522664.3528601
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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: http://doi.org/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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: http://doi.org/10.1109/CVPR.2019.00938
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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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  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: http://doi.org/10.1109/CVPR.2019.00938
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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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  • Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
    Doi: http://doi.org/10.1109/CVPR.2019.00938
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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
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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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  • 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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  • 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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  • 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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  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
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  • Gonzalez, J., Dai, Z., Damianou, A. and Lawrence, ND., 2017. Preferential Bayesian Optimization 34th International Conference on Machine Learning, ICML 2017, v. 3
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  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
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  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
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  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
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  • Grigorievskiy, A., Lawrence, N. and Sarkka, S., 2017. Parallelizable sparse inverse formulation Gaussian processes (SpInGP) 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP),
    Doi: http://doi.org/10.1109/mlsp.2017.8168130
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  • Gonzalez, J., Dai, Z., Damianou, A. and Lawrence, ND., 2017. Preferential Bayesian Optimization 34th International Conference on Machine Learning, ICML 2017, v. 3
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  • Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
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  • Martinez-Hernandez, U., Damianou, A., Camilleri, D., Boorman, LW., Lawrence, N. and Prescott, TJ., 2016. An integrated probabilistic framework for robot perception, learning and memory 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO),
    Doi: http://doi.org/10.1109/robio.2016.7866589
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • Martinez-Hernandez, U., Damianou, A., Camilleri, D., Boorman, LW., Lawrence, N. and Prescott, TJ., 2016. An integrated probabilistic framework for robot perception, learning and memory 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO),
    Doi: http://doi.org/10.1109/robio.2016.7866589
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2016. Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes
    Doi: http://doi.org/10.1007/978-3-319-44412-3_7
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • Camilleri, D., Damianou, A., Jackson, H., Lawrence, N. and Prescott, T., 2016. iCub Visual Memory Inspector: Visualising the iCub’s Thoughts
    Doi: http://doi.org/10.1007/978-3-319-42417-0_5
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • Camilleri, D., Damianou, A., Jackson, H., Lawrence, N. and Prescott, T., 2016. iCub Visual Memory Inspector: Visualising the iCub’s Thoughts
    Doi: http://doi.org/10.1007/978-3-319-42417-0_5
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  • 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,
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  • Camilleri, D., Damianou, A., Jackson, H., Lawrence, N. and Prescott, T., 2016. iCub Visual Memory Inspector: Visualising the iCub’s Thoughts
    Doi: http://doi.org/10.1007/978-3-319-42417-0_5
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
    •
  • Saul, AD., Hensman, J., Vehtari, A. and Lawrence, ND., 2016. Chained Gaussian processes Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
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  • 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,
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  • 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,
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • 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
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  • 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
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  • 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
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  • 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
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  • Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
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  • Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
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  • 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,
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  • 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
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  • Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
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  • 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
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  • 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,
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  • 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
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  • 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
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  • Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
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  • Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
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  • Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
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  • Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
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  • Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
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  • Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
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  • Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
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  • Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
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  • Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
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  • Lee, WB., Ojovan, M. and Jantzen, CM., 2013. Preface
    Doi: http://doi.org/10.1016/b978-0-85709-435-3.50033-1
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  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
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  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
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  • Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
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  • Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
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  • Kalaitzis, AA. and Lawrence, ND., 2012. Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models Proceedings of the 29th International Coference on International Conference on Machine Learning,
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  • 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
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  • 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
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  • 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
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  • Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
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  • 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
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  • 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
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  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
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  • 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
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  • 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
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  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
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  • Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
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  • 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,
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  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
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  • 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,
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  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
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  • Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
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  • 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,
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  • 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,
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  • 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,
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  • 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,
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2010.5589258
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  • Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
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  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
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  • 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,
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  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
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  • 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,
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  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
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  • Titsias, MK., Lawrence, ND. and Rattray, M., 2009. Efficient sampling for Gaussian process inference using control variables Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
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  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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  • 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,
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  • 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,
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
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  • 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,
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  • 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,
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
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  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
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  • Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
    Doi: http://doi.org/10.1145/1553374.1553452
    •
  • 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,
    •
  • Darby, J., Li, B., Costen, N., Fleet, D. and Lawrence, N., 2009. Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery Procedings of the British Machine Vision Conference 2009,
    Doi: http://doi.org/10.5244/c.23.11
    •
  • 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,
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • Calderhead, B., Girolami, M. and Lawrence, ND., 2009. Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Titsias, MK., Lawrence, ND. and Rattray, M., 2009. Efficient sampling for Gaussian process inference using control variables Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
    •
  • Ek, CH., Jaeckel, P., Campbell, N., Lawrence, ND., Melhuish, C., Beji, L., Otmane, S. and Abichou, A., 2009. Shared Gaussian Process Latent Variable Models for Handling Ambiguous Facial Expressions AIP Conference Proceedings,
    Doi: http://doi.org/10.1063/1.3106464
    •
  • 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,
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
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  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
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  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
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  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
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  • Urtasun, R., Fleet, DJ., Geiger, A., Popović, J., Darrell, TJ. and Lawrence, ND., 2008. Topologically-Constrained Latent Variable Models
    Doi: http://doi.org/10.1145/1390156.1390292
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
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  • Ek, CH., Rihan, J., Torr, PHS., Rogez, G. and Lawrence, ND., 2008. Ambiguity modeling in latent spaces Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5237 LNCS
    Doi: http://doi.org/10.1007/978-3-540-85853-9-6
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  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
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  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
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  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
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  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
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  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
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  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
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  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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  • 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
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  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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  • 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
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  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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  • 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
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  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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  • 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
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  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
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  • Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
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  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
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  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
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  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
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  • Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
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  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
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  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
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  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
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  • Laidler, J., Cooke, M. and Lawrence, ND., 2007. Model-driven detection of clean speech patches in noise International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007, v. 3
    •
  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
    •
  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
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  • Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
    Doi: http://doi.org/10.1109/cidm.2007.368886
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  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
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  • Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
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  • 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
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  • 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
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  • 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
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  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
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  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
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  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
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  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
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  • Lawrence, ND., Sanguinetti, G. and Rattray, M., 2006. Modelling transcriptional regulation using Gaussian processes NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems,
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Sanguinetti, G., Rattray, M. and Lawrence, ND., 2006. Identifying Submodules of Cellular Regulatory Networks
    Doi: http://doi.org/10.1007/11885191_11
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
    •
  • Lawrence, ND. and Quiñonero-Candela, J., 2006. Local distance preservation in the GP-LVM through back constraints Proceedings of the 23rd international conference on Machine learning - ICML ’06,
    Doi: http://doi.org/10.1145/1143844.1143909
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  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
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  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
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  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
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  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
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  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
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  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
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  • Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
    •
  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
    •
  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
    •
  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
    •
  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
    Doi: http://doi.org/10.1016/j.neucom.2005.02.016
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2005. System and Method for Replicating Data in a Distributed System
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Abdel-Haleem, YH., Renals, S., Lawrence, ND. and IEEE, , 2004. Acoustic space dimensionality selection and combination using the maximum entropy principle 2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS,
    •
  • Lawrence, ND. and Platt, JC., 2004. Learning to learn with the informative vector machine Twenty-first international conference on Machine learning - ICML ’04,
    Doi: http://doi.org/10.1145/1015330.1015382
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • 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., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., Seeger, MW. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS,
    •
  • Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
    •
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Lawrence, ND., Bishop, CM. and Jordan, MI., 1998. Mixture Representations for Inference and Learning in Boltzmann Machines
  • Bishop, CM., Lawrence, N., Jaakkola, T. and Jordan, MI., 1998. Approximating posterior distributions in belief networks using mixtures Advances in Neural Information Processing Systems,
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Rahman, MA. and Lawrence, ND., A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB ’16,
    Doi: http://doi.org/10.1145/2975167.2985651
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Rahman, MA. and Lawrence, ND., A Gaussian Process Model for Inferring the Dynamic Transcription Factor Activity Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB ’16,
    Doi: http://doi.org/10.1145/2975167.2985651
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Tipping, ME. and Lawrence, ND., A variational approach to robust Bayesian interpolation 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318022
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Urtasun, R., Fleet, DJ. and Lawrence, ND., Modeling Human Locomotion with Topologically Constrained Latent Variable Models
    Doi: http://doi.org/10.1007/978-3-540-75703-0_8
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Lawrence, ND., Milo, M., Niranjan, M., Rashbass, P. and Soullier, S., Bayesian processing of microarray images 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718),
    Doi: http://doi.org/10.1109/nnsp.2003.1318005
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •
  • Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010,
    •
  • Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
  • Rowstron, AIT., Lawrence, N. and Bishop, CM., Probabilistic modelling of replica divergence Proceedings Eighth Workshop on Hot Topics in Operating Systems,
    Doi: http://doi.org/10.1109/hotos.2001.990061
    •
  • Sanguinetti, G., Laidler, J. and Lawrence, ND., Automatic Determination of the Number of Clusters Using Spectral Algorithms 2005 IEEE Workshop on Machine Learning for Signal Processing,
    Doi: http://doi.org/10.1109/mlsp.2005.1532874
    •
  • Vermaak, J., Lawrence, ND. and Perez, P., Variational inference for visual tracking 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.,
    Doi: http://doi.org/10.1109/cvpr.2003.1211431
    •
  • Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
    •

    Journal articles

  • Mishra, C., von Wolff, N., Tripathi, A., Brodie, CN., Lawrence, ND., Ravuri, A., Bremond, É., Preiss, A. and Kumar, A., 2023. Predicting ruthenium catalysed hydrogenation of esters using machine learning Digital Discovery, v. 2
    Doi: http://doi.org/10.1039/d3dd00029j
    •
  • Vargas, F., Ovsianas, A., Fernandes, D., Girolami, M., Lawrence, ND. and Nüsken, N., 2023. Bayesian learning via neural Schrödinger–Föllmer flows Statistics and Computing, v. 33
    Doi: http://doi.org/10.1007/s11222-022-10172-5
    •
  • Vargas, F., Thodoroff, P., Lamacraft, A. and Lawrence, N., 2023. Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134. Entropy (Basel), v. 25
    Doi: http://doi.org/10.3390/e25020289
    •
  • 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
  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
    •
  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
    •
  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
    •
  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2020. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes IEEE Transactions on Industrial Informatics, v. 16
    Doi: http://doi.org/10.1109/TII.2019.2942650
    •
  • 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
    •
  • Wang, B., Li, Z., Dai, Z., Lawrence, N. and Yan, X., 2019. A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant Applied Soft Computing, v. 82
    Doi: http://doi.org/10.1016/j.asoc.2019.105527
    •
  • Niu, M., Cheung, P., Lin, L., Dai, Z., Lawrence, N. and Dunson, D., 2019. Intrinsic Gaussian processes on complex constrained domains Journal of the Royal Statistical Society: Series B (Statistical Methodology), v. 81
    Doi: http://doi.org/10.1111/rssb.12320
    •
  • Prescott, AJ., Camilleri, D., martinez-hernandez, U., Damianou, A. and Lawrence, N., 2019. Memory and Mental Time Travel in Humans and Social Robots Philosophical Transactions B, v. 374
    Doi: http://doi.org/10.1098/rstb.2018.0025
    •
  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
    •
  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
    •
  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
    •
  • Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
    •
  • Särkkä, S., Álvarez, MA. and Lawrence, ND., 2018. Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems IEEE Transactions on Automatic Control, v. 64
    Doi: http://doi.org/10.1109/TAC.2018.2874749
    •
  • Lake, BM., Lawrence, ND. and Tenenbaum, JB., 2018. The Emergence of Organizing Structure in Conceptual Representation Cognitive Science, v. 42
    Doi: http://doi.org/10.1111/cogs.12580
    •
  • Lönnberg, T., Svensson, V., James, KR., Fernandez-Ruiz, D., Sebina, I., Montandon, R., Soon, MSF., Fogg, LG., Nair, AS., Liligeto, UN., Stubbington, MJT., Ly, L-H., Bagger, FO., Zwiessele, M., Lawrence, ND., Souza-Fonseca-Guimaraes, F., Bunn, PT., Engwerda, CR., Heath, WR., Billker, O., Stegle, O., Haque, A. and Teichmann, SA., 2017. Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves TH1/TFH fate bifurcation in malaria Science Immunology, v. 2
    Doi: http://doi.org/10.1126/sciimmunol.aal2192
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Barreto, GA. and Lawrence, ND., 2017. Deep recurrent Gaussian processes for outlier-robust system identification Journal of Process Control, v. 60
    Doi: http://doi.org/10.1016/j.jprocont.2017.06.010
    •
  • Dai, Z., Iqbal, M., Lawrence, ND. and Rattray, M., 2017. Efficient inference for sparse latent variable models of transcriptional regulation Bioinformatics, v. 33
    Doi: http://doi.org/10.1093/bioinformatics/btx508
    •
  • Perdikaris, P., Raissi, M., Damianou, A., Lawrence, ND. and Karniadakis, GE., 2017. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, v. 473
    Doi: http://doi.org/10.1098/rspa.2016.0751
    •
  • Dai, Z., Iqbal, M., Lawrence, ND. and Rattray, M., 2017. Efficient inference for sparse latent variable models of transcriptional regulation Bioinformatics, v. 33
    Doi: http://doi.org/10.1093/bioinformatics/btx508
    •
  • Mattos, CLC., Dai, Z., Damianou, A., Barreto, GA. and Lawrence, ND., 2017. Deep recurrent Gaussian processes for outlier-robust system identification Journal of Process Control, v. 60
    Doi: http://doi.org/10.1016/j.jprocont.2017.06.010
    •
  • Perdikaris, P., Raissi, M., Damianou, A., Lawrence, ND. and Karniadakis, GE., 2017. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, v. 473
    Doi: http://doi.org/10.1098/rspa.2016.0751
    •
  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., 2016. Detecting periodicities with Gaussian processes PeerJ Computer Science, v. 2
    Doi: http://doi.org/10.7717/peerj-cs.50
    •
  • Durrande, N., Hensman, J., Rattray, M. and Lawrence, ND., 2016. Detecting periodicities with Gaussian processes PeerJ Computer Science, v. 2
    Doi: http://doi.org/10.7717/peerj-cs.50
    •
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2016. Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes Journal of Machine Learning Research, v. 17
    Doi: http://doi.org/10.5555/2946645.2946687
    •
  • Damianou, AC., Titsias, MK. and Lawrence, ND., 2016. Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes Journal of Machine Learning Research, v. 17
    Doi: http://doi.org/10.5555/2946645.2946687
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    •
  • Zampini, V., Johnson, SL., Franz, C., Lawrence, ND., Knipper, M., Magistretti, J., Masetto, S., Marcotti, W., Münkner, S. and Engel, J., 2010. Elementary properties of CaV1.3 Ca2+ channels expressed in mouse cochlear inner hair cells The Journal of Physiology, v. 588
    Doi: http://doi.org/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: http://doi.org/10.1186/1471-2105-10-211
    •
  • 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: http://doi.org/10.1186/1471-2105-10-211
    •
  • 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: http://doi.org/10.1186/1471-2105-10-211
    •
  • 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: http://doi.org/10.1186/1471-2105-10-211
    •
  • 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: http://doi.org/10.1186/1471-2105-10-211
    •
  • 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: http://doi.org/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: http://doi.org/10.1093/bioinformatics/btn278
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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: http://doi.org/10.1093/bioinformatics/btn278
    •
  • 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: http://doi.org/10.1093/bioinformatics/btn278
    •
  • 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: http://doi.org/10.1093/bioinformatics/btn278
    •
  • 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: http://doi.org/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: http://doi.org/10.1093/bioinformatics/btl473
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl361
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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: http://doi.org/10.1093/bioinformatics/btl473
    •
  • Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, ND., 2006. Propagating Uncertainty in Microarray Data Analysis Briefings in Bioinformatics, v. 7
    Doi: http://doi.org/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: http://doi.org/10.1093/bioinformatics/btl361
    •
  • Sanguinetti, G., Lawrence, ND. and Rattray, M., 2006. Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities Bioinformatics, v. 22
    Doi: http://doi.org/10.1093/bioinformatics/btl473
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl361
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl361
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl154
    •
  • na-Centeno, TP. and Lawrence, ND., 2006. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis Journal of Machine Learning Research, v. 7
    •
  • na-Centeno, TP. and Lawrence, ND., 2006. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis Journal of Machine Learning Research, v. 7
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl154
    •
  • na-Centeno, TP. and Lawrence, ND., 2006. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis Journal of Machine Learning Research, v. 7
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl154
    •
  • na-Centeno, TP. and Lawrence, ND., 2006. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis Journal of Machine Learning Research, v. 7
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl154
    •
  • na-Centeno, TP. and Lawrence, ND., 2006. Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis Journal of Machine Learning Research, v. 7
    •
  • Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, ND., 2006. Propagating Uncertainty in Microarray Data Analysis Briefings in Bioinformatics, v. 7
    Doi: http://doi.org/10.1093/bib/bbk003
    •
  • Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, ND., 2006. Propagating Uncertainty in Microarray Data Analysis Briefings in Bioinformatics, v. 7
    Doi: http://doi.org/10.1093/bib/bbk003
    •
  • Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, ND., 2006. Propagating Uncertainty in Microarray Data Analysis Briefings in Bioinformatics, v. 7
    Doi: http://doi.org/10.1093/bib/bbk003
    •
  • Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, ND., 2006. Propagating Uncertainty in Microarray Data Analysis Briefings in Bioinformatics, v. 7
    Doi: http://doi.org/10.1093/bib/bbk003
    •
  • Sanguinetti, G., Lawrence, ND. and Rattray, M., 2006. Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities Bioinformatics, v. 22
    Doi: http://doi.org/10.1093/bioinformatics/btl473
    •
  • Sanguinetti, G., Lawrence, ND. and Rattray, M., 2006. Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities Bioinformatics, v. 22
    Doi: http://doi.org/10.1093/bioinformatics/btl473
    •
  • Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, ND., 2006. Propagating Uncertainty in Microarray Data Analysis Briefings in Bioinformatics, v. 7
    Doi: http://doi.org/10.1093/bib/bbk003
    •
  • Sanguinetti, G., Lawrence, ND. and Rattray, M., 2006. Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities Bioinformatics, v. 22
    Doi: http://doi.org/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: http://doi.org/10.1093/bioinformatics/btl154
    •
  • 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: http://doi.org/10.1093/bioinformatics/btl361
    •
  • Lawrence, ND., 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 Bionformatics, v. 21
    Doi: http://doi.org/10.1093/bioinformatics/bti617
    •
  • Lawrence, ND., 2005. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models Journal of Machine Learning Research, v. 6
    •
  • Lawrence, ND., 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 Bionformatics, v. 21
    Doi: http://doi.org/10.1093/bioinformatics/bti617
    •
  • 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: http://doi.org/10.1093/bioinformatics/bti583
    •
  • Lawrence, ND., 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 Bionformatics, v. 21
    Doi: http://doi.org/10.1093/bioinformatics/bti617
    •
  • Sanguinetti, G., Milo, M., Rattray, M. and Lawrence, ND., 2005. Accounting for Probe-level Noise in Principal Component Analysis of Microarray Data Bionformatics, v. 21
    Doi: http://doi.org/10.1093/bioinformatics/bti617
    •
  • Sanguinetti, G., Milo, M., Rattray, M. and Lawrence, ND., 2005. Accounting for Probe-level Noise in Principal Component Analysis of Microarray Data Bionformatics, v. 21
    Doi: http://doi.org/10.1093/bioinformatics/bti617
    •
  • 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: http://doi.org/10.1093/bioinformatics/bti583
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  • Lawrence, ND., 2005. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models Journal of Machine Learning Research, v. 6
    •
  • 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: http://doi.org/10.1093/bioinformatics/bti583
    •
  • 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: http://doi.org/10.1093/bioinformatics/bti583
    •
  • 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: http://doi.org/10.1093/bioinformatics/bti583
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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: http://doi.org/10.1093/bioinformatics/btg438
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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: http://doi.org/10.1093/bioinformatics/btg438
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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: http://doi.org/10.1093/bioinformatics/btg438
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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: http://doi.org/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 Biochemical Transations, v. 31
    •
  • Milo, M., Fazeli, A., Niranjan, M. and Lawrence, ND., 2003. A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays Biochemical Transations, v. 31
    •
  • Milo, M., Fazeli, A., Niranjan, M. and Lawrence, ND., 2003. A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays Biochemical Transations, v. 31
    •
  • Milo, M., Fazeli, A., Niranjan, M. and Lawrence, ND., 2003. A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays Biochemical Transations, v. 31
    •
  • Milo, M., Fazeli, A., Niranjan, M. and Lawrence, ND., 2003. A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays Biochemical Transations, v. 31
    •
  • 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
    •
  • 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
    •
  • 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
    •
  • 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
    •
  • Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
    •
  • Paleyes, A., Urma, R-G. and Lawrence, ND., Challenges in Deploying Machine Learning: a Survey of Case Studies ACM Computing Surveys,
    •
  • Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
    •
  • Vargas, F., Thodoroff, P., Lamacraft, A. and Lawrence, N., Solving Schrödinger Bridges via Maximum Likelihood Entropy (Basel), v. 23
    Doi: 10.3390/e23091134
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  • Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
    •
  • Zwiessele, M. and Lawrence, ND., Topslam: Waddington Landscape Recovery for Single Cell Experiments
    Doi: http://doi.org/10.1101/057778
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  • Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
    •
  • Zwiessele, M. and Lawrence, ND., Topslam: Waddington Landscape Recovery for Single Cell Experiments
    Doi: http://doi.org/10.1101/057778
    •
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Lawrence, ND., Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
  • Lawrence, ND., Living Together: Mind and Machine Intelligence
  • Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian Processes Journal of Machine Learning Research, v. 12 (2011)
    •
  • Lawrence, ND., Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
  • Lawrence, ND., Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
  • Lawrence, ND., Data Readiness Levels
  • Lawrence, ND., Data Readiness Levels
  • Lawrence, ND., Data Readiness Levels
  • Lawrence, ND., Data Readiness Levels
  • Lawrence, ND., Data Readiness Levels

    • Internet publications

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

    • Other publications

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

    • Reports

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

    • Book chapters

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

    Theses / dissertations

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

    • Books

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

    Software

  • Lawrence, ND., MOCAP Toolbox for MATLAB

    • Contact Details

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

    ndl21 at cam dot ac dot uk

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