Conference proceedings
Paleyes, A., Li, HB. and Lawrence, ND., 2024. Can causality accelerate experimentation in software systems? Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024,
Doi: http://doi.org/10.1145/3644815.3644985
Cabrera, C., Paleyes, A. and Lawrence, ND., 2024. Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation Proceedings - 2024 IEEE/ACM International Workshop New Trends in Software Architecture, SATrends 2024,
Doi: 10.1145/3643657.3643910
Paleyes, A. and Lawrence, ND., 2023. Causal fault localisation in dataflow systems Proceedings of the 3rd Workshop on Machine Learning and Systems, v. 4
Doi: 10.1145/3578356.3592593
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
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
Lalchand, V., Ravuri, A. and Lawrence, ND., 2022. Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference Proceedings of Machine Learning Research, v. 151
Li, S., López-García, M., Lawrence, ND. and Cutillo, L., 2022. Two-way Sparse Network Inference for Count Data Proceedings of Machine Learning Research, v. 151
Bell, SJ., Kampman, OP., Dodge, J. and Lawrence, ND., 2022. Modeling the Machine Learning Multiverse Advances in Neural Information Processing Systems, v. 35
Lalchand, V., Ravuri, A., Dann, E., Kumasaka, N., Sumanaweera, D., Lindeboom, RGH., Madad, S., Teichmann, SA. and Lawrence, ND., 2022. Modelling Technical and Biological Effects in single-cell RNA-seq data with Scalable Gaussian Process Latent Variable Models (GPLVMs) Proceedings of Machine Learning Research, v. 200
Thodoroff, P., Li, W. and Lawrence, ND., 2022. Benchmarking Real-Time Reinforcement Learning Proceedings of Machine Learning Research, v. 181
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,
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
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
Ahn, S., Hu, SX., Damianou, A., Lawrence, ND. and Dai, Z., 2019. Variational information distillation for knowledge transfer Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, v. 2019-June
Doi: 10.1109/CVPR.2019.00938
Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
Flennerhag, S., Lawrence, ND., Moreno, PG. and Damianou, A., 2019. Transferring knowledge across learning processes 7th International Conference on Learning Representations, ICLR 2019,
Klein, A., Dai, Z., Hutter, F., Lawrence, N. and González, J., 2019. Meta-surrogate benchmarking for hyperparameter optimization Advances in Neural Information Processing Systems, v. 32
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
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
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
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
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
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,
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,
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,
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,
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,
Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
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
Gonzalez, J., Dai, Z., Damianou, A. and Lawrence, ND., 2017. Preferential Bayesian Optimization 34th International Conference on Machine Learning, ICML 2017, v. 3
Dai, Z., Álvarez, MA. and Lawrence, ND., 2017. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes. NIPS,
Gonzalez, J., Dai, Z., Damianou, A. and Lawrence, ND., 2017. Preferential Bayesian Optimization 34th International Conference on Machine Learning, ICML 2017, v. 3
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,
Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
Martinez-Hernandez, U., Damianou, A., Camilleri, D., Boorman, LW., Lawrence, N. and Prescott, TJ., 2016. An integrated probabilistic framework for robot perception, learning and memory 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO),
Doi: http://doi.org/10.1109/robio.2016.7866589
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,
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,
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,
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,
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,
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,
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,
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,
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,
Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
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,
Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
González, J., Osborne, M. and Lawrence, ND., 2016. GLASSES: Relieving the myopia of Bayesian optimisation Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
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,
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,
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,
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,
González, J., Dai, Z., Hennig, P. and Lawrence, N., 2016. Batch bayesian optimization via local penalization Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
Dai, Z., Damianou, A., González, J. and Lawrence, N., 2016. Variational auto-encoded deep Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
González, J., Dai, Z., Hennig, P. and Lawrence, N., 2016. Batch bayesian optimization via local penalization Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016,
Mattos, CLC., Dai, Z., Damianou, A., Forth, J., Barreto, GA. and Lawrence, ND., 2016. Recurrent Gaussian processes 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings,
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
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,
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
Vanschoren, J., Bischl, B., Hutter, F., Sebag, M., Kegl, B., Schmid, M., Napolitano, G., Wolstencroft, K., Williams, AR. and Lawrence, N., 2015. Towards a data science collaboratory Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9385
Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2015. Monitoring short term changes of malaria incidence in Uganda with Gaussian processes CEUR Workshop Proceedings, v. 1425
Damianou, A. and Lawrence, ND., 2015. Semi-described and semi-supervised learning with Gaussian processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015,
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
Damianou, A. and Lawrence, ND., 2015. Semi-described and semi-supervised learning with Gaussian processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015,
Vanschoren, J., Bischl, B., Hutter, F., Sebag, M., Kegl, B., Schmid, M., Napolitano, G., Wolstencroft, K., Williams, AR. and Lawrence, N., 2015. Towards a data science collaboratory Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9385
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
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,
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
Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, N., 2015. Monitoring short term changes of malaria incidence in Uganda with Gaussian processes CEUR Workshop Proceedings, v. 1425
Damianou, A. and Lawrence, ND., 2015. Semi-described and semi-supervised learning with Gaussian processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015,
Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
Tosi, A., Hauberg, S., Vellido, A. and Lawrence, ND., 2014. Metrics for probabilistic geometries Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014,
Andrade-Pacheco, R., Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Hybrid discriminative-generative approach with Gaussian processes Journal of Machine Learning Research, v. 33
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
Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Tilted variational bayes Journal of Machine Learning Research, v. 33
Tosi, A., Hauberg, S., Vellido, A. and Lawrence, ND., 2014. Metrics for probabilistic geometries Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014,
Andrade-Pacheco, R., Hensman, J., Zwießele, M. and Lawrence, ND., 2014. Hybrid discriminative-generative approach with Gaussian processes Journal of Machine Learning Research, v. 33
Cohn, T., Preotiuc-Pietro, D. and Lawrence, N., 2014. Gaussian Processes for Natural Language Processing Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials,
Doi: 10.3115/v1/p14-6001
Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
Kalaitzis, A., Lafferty, J., Lawrence, ND. and Zhou, S., 2013. The bigraphical lasso 30th International Conference on Machine Learning, ICML 2013,
Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
Damianou, AC. and Lawrence, ND., 2013. Deep Gaussian processes Journal of Machine Learning Research, v. 31
Hensman, J., Fusi, N. and Lawrence, ND., 2013. Gaussian processes for big data Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013,
Damianou, AC., Ek, CH., Titsias, MK. and Lawrence, ND., 2012. Manifold relevance determination Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
Damianou, AC., Ek, CH., Titsias, MK. and Lawrence, ND., 2012. Manifold relevance determination Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
Kalaitzis, AA. and Lawrence, ND., 2012. Residual Component Analysis: Generalising PCA for more flexible inference in linear-Gaussian models Proceedings of the 29th International Coference on International Conference on Machine Learning,
Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
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
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
Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
Damianou, AC., Ek, CH., Titsias, MK. and Lawrence, ND., 2012. Manifold relevance determination Proceedings of the 29th International Conference on Machine Learning, ICML 2012, v. 1
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
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
Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
Hensman, J., Rattray, M. and Lawrence, ND., 2012. Fast variational inference in the conjugate exponential family Advances in Neural Information Processing Systems, v. 4
Lawrence, N. and Girolami, M., 2012. Preface Journal of Machine Learning Research, v. 22
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,
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,
Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
Lawrence, ND., 2011. Spectral dimensionality reduction via maximum entropy Journal of Machine Learning Research, v. 15
Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
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,
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,
Damianou, AC., Titsias, MK. and Lawrence, ND., 2011. Variational Gaussian process dynamical systems Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K., 2011. Efficient inference in matrix-variate Gaussian models with iid observation noise Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
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,
Stegle, O., Lippert, C., Mooij, J., Lawrence, N. and Borgwardt, K., 2011. Efficient inference in matrix-variate Gaussian models with iid observation noise Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011,
Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
Honkela, A., Milo, M., Holley, M., Rattray, M. and Lawrence, ND., 2010. Ranking of gene regulators through differential equations and Gaussian processes 2010 IEEE International Workshop on Machine Learning for Signal Processing,
Doi: http://doi.org/10.1109/mlsp.2010.5589258
Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
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
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
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
Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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
Álvarez, MA., Luengo, D., Titsias, MK. and Lawrence, ND., 2010. Efficient multioutput gaussian processes through variational inducing kernels Journal of Machine Learning Research, v. 9
Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
Álvarez, MA., Peters, J., Schölkopf, B. and Lawrence, ND., 2010. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010,
Titsias, MK. and Lawrence, ND., 2010. Bayesian Gaussian process latent variable model Journal of Machine Learning Research, v. 9
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
Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with gaussian processes ACM International Conference Proceeding Series, v. 382
Doi: http://doi.org/10.1145/1553374.1553452
Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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
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
Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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
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
Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
Lawrence, ND. and Urtasun, R., 2009. Non-linear matrix factorization with Gaussian processes Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09,
Doi: http://doi.org/10.1145/1553374.1553452
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
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,
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
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., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
Alvarez, M. and Lawrence, ND., 2009. Sparse convolved Gaussian processes for multi-output regression Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference,
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
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
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,
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
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
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,
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,
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,
Alvarez, M., Luengo, D. and Lawrence, ND., 2009. Latent force models Journal of Machine Learning Research, v. 5
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,
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,
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,
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,
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,
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
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
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
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
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
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
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
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
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
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
Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
Lawrence, ND., Sanguinetti, G. and Rattray, M., 2007. Modelling transcriptional regulation using Gaussian processes Advances in Neural Information Processing Systems,
Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
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
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
Ferris, B., Fox, D. and Lawrence, N., 2007. WiFi-SLAM using Gaussian process latent variable models IJCAI International Joint Conference on Artificial Intelligence,
Laidler, J., Cooke, M. and Lawrence, ND., 2007. Model-driven detection of clean speech patches in noise International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007, v. 3
Eciolaza, L., Alkarouri, M., Lawrence, ND., Kadirkamanathan, V. and Fleming, PJ., 2007. Gaussian Process Latent Variable Models for Fault Detection 2007 IEEE Symposium on Computational Intelligence and Data Mining,
Doi: http://doi.org/10.1109/cidm.2007.368886
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
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
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
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
Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
Lawrence, ND. and Moore, AJ., 2007. Hierarchical Gaussian process latent variable models ICML 2007 - Proceedings of the 24th International Conference on Machine Learning,
Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
Lawrence, ND., 2007. Learning for larger datasets with the Gaussian process latent variable model Journal of Machine Learning Research, v. 2
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 ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, v. 2006
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
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
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
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
Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
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
Hifny, Y., Renais, S. and Lawrence, ND., 2005. A hybrid MaxEnt/HMM based ASR system 9th European Conference on Speech Communication and Technology,
Lawrence, ND. and Jordan, MI., 2005. Semi-supervised Learning via gaussian processes Advances in Neural Information Processing Systems,
Tipping, ME. and Lawrence, ND., 2005. Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis Neurocomputing, v. 69
Doi: 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
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
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
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
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
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,
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., 2004. Gaussian process latent variable models for visualisation of high dimensional data Advances in Neural Information Processing Systems,
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,
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., 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., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
Lawrence, N., Seeger, M. and Herbrich, R., 2002. Fast Sparse Gaussian Process Methods: The Informative Vector Machine NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems,
Lawrence, ND., Rowstron, AIT., Bishop, CM. and Taylor, MJ., 2002. Optimising synchronisation times for mobile devices Advances in Neural Information Processing Systems,
Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
Lawrence, ND., 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., 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,
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,
Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
Lawrence, ND., 2002. Node relevance determination NEURAL NETS WIRN VIETRI-01,
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,
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,
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
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
Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
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
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. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
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
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
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
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., Gaussian Process Models for Visualisation of High Dimensional Data
Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
Lawrence, ND. and Schölkopf, B., Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
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., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
Lawrence, ND., Variational Learning for Multi-layer networks of Linear Threshold Units Eighth International Workshop on Artificial Intelligence and Statistics,
Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
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,
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,
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
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,
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
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,
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
Lawrence, ND., Gaussian Process Models for Visualisation of High Dimensional Data
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,
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
Seeger, M., Williams, CKI. and Lawrence, ND., Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
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
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
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
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., 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
Journal articles
Lawrence, ND. and Montgomery, J., 2024. Accelerating AI for science: open data science for science. R Soc Open Sci, v. 11
Doi: 10.1098/rsos.231130
Mishra, C., von Wolff, N., Tripathi, A., Brodie, CN., Lawrence, ND., Ravuri, A., Bremond, É., Preiss, A. and Kumar, A., 2023. Predicting ruthenium catalysed hydrogenation of esters using machine learning Digital Discovery, v. 2
Doi: 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
Ross, J., Fridman, M., Kelepouris, N., Murray, K., Krone, N., Polak, M., Rohrer, TR., Pietropoli, A., Lawrence, N. and Backeljauw, P., 2023. Factors Associated With Response to Growth Hormone in Pediatric Growth Disorders: Results of a 5-year Registry Analysis. J Endocr Soc, v. 7
Doi: http://doi.org/10.1210/jendso/bvad026
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: 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: http://doi.org/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
Delacroix, S. and Lawrence, N., 2019. Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance International Data Privacy Law,
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,
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
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
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
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
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
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
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
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
Mattos, CLC., Damianou, A., Barreto, GA. and Lawrence, ND., 2016. Latent Autoregressive Gaussian Processes Models for Robust System Identification IFAC-PapersOnLine, v. 49
Doi: http://doi.org/10.1016/j.ifacol.2016.07.353
Mattos, CLC., Damianou, A., Barreto, GA. and Lawrence, ND., 2016. Latent Autoregressive Gaussian Processes Models for Robust System Identification IFAC-PapersOnLine, v. 49
Doi: http://doi.org/10.1016/j.ifacol.2016.07.353
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
Gonzalez-Hernandez, J., Daoud, Y., Styers, J., Journeycake, JM., Channabasappa, N. and Piper, HG., 2016. Central venous thrombosis in children with intestinal failure on long-term parenteral nutrition. J Pediatr Surg, v. 51
Doi: http://doi.org/10.1016/j.jpedsurg.2016.02.024
Mattos, CLC., Damianou, A., Barreto, GA. and Lawrence, ND., 2016. Latent Autoregressive Gaussian Processes Models for Robust System Identification IFAC-PapersOnLine, v. 49
Doi: http://doi.org/10.1016/j.ifacol.2016.07.353
Hensman, J., Rattray, M. and Lawrence, ND., 2015. Fast Nonparametric Clustering of Structured Time-Series IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 37
Doi: http://doi.org/10.1109/tpami.2014.2318711
Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2015. Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays Proceedings of the National Academy of Sciences, v. 112
Doi: http://doi.org/10.1073/pnas.1420404112
Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2015. Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays Proceedings of the National Academy of Sciences, v. 112
Doi: http://doi.org/10.1073/pnas.1420404112
Hensman, J., Rattray, M. and Lawrence, ND., 2015. Fast Nonparametric Clustering of Structured Time-Series IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 37
Doi: http://doi.org/10.1109/tpami.2014.2318711
Gambardella, G., Peluso, I., Montefusco, S., Bansal, M., Medina, DL., Lawrence, N. and di Bernardo, D., 2015. A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data BMC Bioinformatics, v. 16
Doi: http://doi.org/10.1186/s12859-015-0700-3
Gambardella, G., Peluso, I., Montefusco, S., Bansal, M., Medina, DL., Lawrence, N. and di Bernardo, D., 2015. A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data BMC Bioinformatics, v. 16
Doi: http://doi.org/10.1186/s12859-015-0700-3
Gambardella, G., Peluso, I., Montefusco, S., Bansal, M., Medina, DL., Lawrence, N. and di Bernardo, D., 2015. A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data BMC Bioinformatics, v. 16
Doi: http://doi.org/10.1186/s12859-015-0700-3
Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2015. Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays Proceedings of the National Academy of Sciences, v. 112
Doi: http://doi.org/10.1073/pnas.1420404112
Hensman, J., Rattray, M. and Lawrence, ND., 2015. Fast Nonparametric Clustering of Structured Time-Series IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 37
Doi: http://doi.org/10.1109/tpami.2014.2318711
wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
Doi: http://doi.org/10.1371/journal.pcbi.1003598
Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, ND., 2014. Consistent mapping of government malaria records across a changing territory delimitation Malaria Journal, v. 13
Doi: http://doi.org/10.1186/1475-2875-13-s1-p5
wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
Doi: http://doi.org/10.1371/journal.pcbi.1003598
Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
Doi: http://doi.org/10.1038/ncomms5890
Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, ND., 2014. Consistent mapping of government malaria records across a changing territory delimitation Malaria Journal, v. 13
Doi: http://doi.org/10.1186/1475-2875-13-s1-p5
wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
Doi: http://doi.org/10.1371/journal.pcbi.1003598
wa Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, HG., Reid, G., Lawrence, ND. and Rattray, M., 2014. Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data PLoS Computational Biology, v. 10
Doi: http://doi.org/10.1371/journal.pcbi.1003598
Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
Doi: http://doi.org/10.1038/ncomms5890
Andrade-Pacheco, R., Mubangizi, M., Quinn, J. and Lawrence, ND., 2014. Consistent mapping of government malaria records across a changing territory delimitation Malaria Journal, v. 13
Doi: http://doi.org/10.1186/1475-2875-13-s1-p5
Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
Doi: http://doi.org/10.1038/ncomms5890
Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., 2014. Warped linear mixed models for the genetic analysis of transformed phenotypes Nature Communications, v. 5
Doi: http://doi.org/10.1038/ncomms5890
Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
Doi: http://doi.org/10.1109/tpami.2013.86
Hensman, J., Lawrence, ND. and Rattray, M., 2013. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters BMC Bioinformatics, v. 14
Doi: http://doi.org/10.1186/1471-2105-14-252
Hensman, J., Lawrence, ND. and Rattray, M., 2013. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters BMC Bioinformatics, v. 14
Doi: http://doi.org/10.1186/1471-2105-14-252
Brockington, A., Ning, K., Heath, PR., Wood, E., Kirby, J., Fusi, N., Lawrence, N., Wharton, SB., Ince, PG. and Shaw, PJ., 2013. Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity Acta Neuropathologica, v. 125
Doi: http://doi.org/10.1007/s00401-012-1058-5
Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data Methods in Molecular Biology, v. 939
Doi: http://doi.org/10.1007/978-1-62703-107-3-6
Brockington, A., Ning, K., Heath, PR., Wood, E., Kirby, J., Fusi, N., Lawrence, N., Wharton, SB., Ince, PG. and Shaw, PJ., 2013. Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity Acta Neuropathologica, v. 125
Doi: http://doi.org/10.1007/s00401-012-1058-5
Brockington, A., Ning, K., Heath, PR., Wood, E., Kirby, J., Fusi, N., Lawrence, N., Wharton, SB., Ince, PG. and Shaw, PJ., 2013. Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity Acta Neuropathologica, v. 125
Doi: http://doi.org/10.1007/s00401-012-1058-5
Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data Methods in Molecular Biology, v. 939
Doi: http://doi.org/10.1007/978-1-62703-107-3-6
Nardo, G., Iennaco, R., Fusi, N., Heath, PR., Marino, M., Trolese, MC., Ferraiuolo, L., Lawrence, N., Shaw, PJ. and Bendotti, C., 2013. Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis Brain, v. 136
Doi: http://doi.org/10.1093/brain/awt250
Honkela, A., Rattray, M. and Lawrence, ND., 2013. Mining regulatory network connections by ranking transcription factor target genes using time series expression data Methods in Molecular Biology, v. 939
Doi: http://doi.org/10.1007/978-1-62703-107-3-6
Nardo, G., Iennaco, R., Fusi, N., Heath, PR., Marino, M., Trolese, MC., Ferraiuolo, L., Lawrence, N., Shaw, PJ. and Bendotti, C., 2013. Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis Brain, v. 136
Doi: http://doi.org/10.1093/brain/awt250
Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
Doi: http://doi.org/10.1109/tpami.2013.86
Fusi, N., Lippert, C., Borgwardt, K., Lawrence, ND. and Stegle, O., 2013. Detecting regulatory gene–environment interactions with unmeasured environmental factors Bioinformatics, v. 29
Doi: http://doi.org/10.1093/bioinformatics/btt148
Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
Doi: http://doi.org/10.1109/tpami.2013.86
Nardo, G., Iennaco, R., Fusi, N., Heath, PR., Marino, M., Trolese, MC., Ferraiuolo, L., Lawrence, N., Shaw, PJ. and Bendotti, C., 2013. Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis Brain, v. 136
Doi: http://doi.org/10.1093/brain/awt250
Fusi, N., Lippert, C., Borgwardt, K., Lawrence, ND. and Stegle, O., 2013. Detecting regulatory gene–environment interactions with unmeasured environmental factors Bioinformatics, v. 29
Doi: http://doi.org/10.1093/bioinformatics/btt148
Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
Doi: http://doi.org/10.1109/tpami.2013.86
Hensman, J., Lawrence, ND. and Rattray, M., 2013. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters BMC Bioinformatics, v. 14
Doi: http://doi.org/10.1186/1471-2105-14-252
Alvarez, MA., Luengo, D. and Lawrence, ND., 2013. Linear Latent Force Models Using Gaussian Processes IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35
Doi: http://doi.org/10.1109/tpami.2013.86
Fusi, N., Lippert, C., Borgwardt, K., Lawrence, ND. and Stegle, O., 2013. Detecting regulatory gene–environment interactions with unmeasured environmental factors Bioinformatics, v. 29
Doi: http://doi.org/10.1093/bioinformatics/btt148
Lawrence, ND., 2012. A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models Journal of Machine Learning Research, v. 13
Doi: http://doi.org/10.5555/2503308.2343695
Penfold, CA., Brown, PE., Lawrence, ND. and Goldman, ASH., 2012. Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition PLoS Computational Biology, v. 8
Doi: http://doi.org/10.1371/journal.pcbi.1002496
Penfold, CA., Brown, PE., Lawrence, ND. and Goldman, ASH., 2012. Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition PLoS Computational Biology, v. 8
Doi: http://doi.org/10.1371/journal.pcbi.1002496
Donaldson, IJ., Amin, S., Hensman, JJ., Kutejova, E., Rattray, M., Lawrence, N., Hayes, A., Ward, CM. and Bobola, N., 2012. Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development Nucleic Acids Research, v. 40
Doi: http://doi.org/10.1093/nar/gkr1240
Donaldson, IJ., Amin, S., Hensman, JJ., Kutejova, E., Rattray, M., Lawrence, N., Hayes, A., Ward, CM. and Bobola, N., 2012. Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development Nucleic Acids Research, v. 40
Doi: http://doi.org/10.1093/nar/gkr1240
Fusi, N., Stegle, O. and Lawrence, ND., 2012. Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies PLoS Computational Biology, v. 8
Doi: http://doi.org/10.1371/journal.pcbi.1002330
Fusi, N., Stegle, O. and Lawrence, ND., 2012. Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies PLoS Computational Biology, v. 8
Doi: http://doi.org/10.1371/journal.pcbi.1002330
Titsias, MK., Honkela, A., Lawrence, ND. and Rattray, M., 2012. Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison BMC Systems Biology, v. 6
Doi: http://doi.org/10.1186/1752-0509-6-53
Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
Doi: 10.1109/TPAMI.2012.14
Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
Doi: http://doi.org/10.1109/TPAMI.2012.14
Lázaro-Gredilla, M., Van Vaerenbergh, S. and Lawrence, ND., 2012. Overlapping Mixtures of Gaussian Processes for the data association problem Pattern Recognition, v. 45
Doi: http://doi.org/10.1016/j.patcog.2011.10.004
Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
Doi: http://doi.org/10.1109/TPAMI.2012.14
Lázaro-Gredilla, M., Van Vaerenbergh, S. and Lawrence, ND., 2012. Overlapping Mixtures of Gaussian Processes for the data association problem Pattern Recognition, v. 45
Doi: http://doi.org/10.1016/j.patcog.2011.10.004
Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
Doi: http://doi.org/10.1109/TPAMI.2012.14
Penfold, CA., Brown, PE., Lawrence, ND. and Goldman, ASH., 2012. Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition PLoS Computational Biology, v. 8
Doi: http://doi.org/10.1371/journal.pcbi.1002496
Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Untitled IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 34
Doi: http://doi.org/10.1109/TPAMI.2012.14
Donaldson, IJ., Amin, S., Hensman, JJ., Kutejova, E., Rattray, M., Lawrence, N., Hayes, A., Ward, CM. and Bobola, N., 2012. Genome-wide occupancy links Hoxa2 to Wnt–β-catenin signaling in mouse embryonic development Nucleic Acids Research, v. 40
Doi: http://doi.org/10.1093/nar/gkr1240
Titsias, MK., Honkela, A., Lawrence, ND. and Rattray, M., 2012. Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison BMC Systems Biology, v. 6
Doi: http://doi.org/10.1186/1752-0509-6-53
Zabih, R., Kang, SB., Lawrence, N., Matas, J. and Welling, M., 2012. Editor's note IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 34
Doi: http://doi.org/10.1109/TPAMI.2012.75
Fusi, N., Stegle, O. and Lawrence, ND., 2012. Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies PLoS Computational Biology, v. 8
Doi: http://doi.org/10.1371/journal.pcbi.1002330
Titsias, MK., Honkela, A., Lawrence, ND. and Rattray, M., 2012. Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison BMC Systems Biology, v. 6
Doi: http://doi.org/10.1186/1752-0509-6-53
Lawrence, ND., 2012. A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models Journal of Machine Learning Research, v. 13
Doi: http://doi.org/10.5555/2503308.2343695
Lawrence, ND., 2012. A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models Journal of Machine Learning Research, v. 13
Doi: http://doi.org/10.5555/2503308.2343695
Lázaro-Gredilla, M., Van Vaerenbergh, S. and Lawrence, ND., 2012. Overlapping Mixtures of Gaussian Processes for the data association problem Pattern Recognition, v. 45
Doi: http://doi.org/10.1016/j.patcog.2011.10.004
Álvarez, MA. and Lawrence, ND., 2011. Computationally efficient convolved multiple output gaussian processes Journal of Machine Learning Research, v. 12
Honkela, A., Gao, P., Ropponen, J., Rattray, M. and Lawrence, ND., 2011. tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor Bioinformatics, v. 27
Doi: http://doi.org/10.1093/bioinformatics/btr057
Álvarez, MA. and Lawrence, ND., 2011. Computationally efficient convolved multiple output gaussian processes Journal of Machine Learning Research, v. 12
Honkela, A., Gao, P., Ropponen, J., Rattray, M. and Lawrence, ND., 2011. tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor Bioinformatics, v. 27
Doi: http://doi.org/10.1093/bioinformatics/btr057
Álvarez, MA. and Lawrence, ND., 2011. Computationally efficient convolved multiple output gaussian processes Journal of Machine Learning Research, v. 12
Honkela, A., Gao, P., Ropponen, J., Rattray, M. and Lawrence, ND., 2011. tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor Bioinformatics, v. 27
Doi: http://doi.org/10.1093/bioinformatics/btr057
Kalaitzis, AA. and Lawrence, ND., 2011. A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression BMC Bioinformatics, v. 12
Doi: http://doi.org/10.1186/1471-2105-12-180
Kalaitzis, AA. and Lawrence, ND., 2011. A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression BMC Bioinformatics, v. 12
Doi: http://doi.org/10.1186/1471-2105-12-180
Álvarez, MA. and Lawrence, ND., 2011. Computationally efficient convolved multiple output gaussian processes Journal of Machine Learning Research, v. 12
Kalaitzis, AA. and Lawrence, ND., 2011. A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression BMC Bioinformatics, v. 12
Doi: http://doi.org/10.1186/1471-2105-12-180
Álvarez, MA., Rosasco, L. and Lawrence, ND., 2011. Kernels for Vector-Valued Functions: a Review CoRR, v. abs/1106.6251
Álvarez, MA., Rosasco, L. and Lawrence, ND., 2011. Kernels for Vector-Valued Functions: a Review CoRR, v. abs/1106.6251
Álvarez, MA., Rosasco, L. and Lawrence, ND., 2011. Kernels for Vector-Valued Functions: a Review CoRR, v. abs/1106.6251
Álvarez, MA. and Lawrence, ND., 2011. Computationally efficient convolved multiple output gaussian processes Journal of Machine Learning Research, v. 12
Álvarez, MA., Rosasco, L. and Lawrence, ND., 2011. Kernels for Vector-Valued Functions: a Review CoRR, v. abs/1106.6251
Honkela, A., Girardot, C., Gustafson, EH., Liu, Y-H., Furlong, EEM., Lawrence, ND. and Rattray, M., 2010. Model-based Method for Transcription Factor Target Identification with Limited Data Proc. Natl. Acad. Sci. USA, v. 107
Doi: http://doi.org/10.1073/pnas.0914285107
Honkela, A., Girardot, C., Gustafson, EH., Liu, Y-H., Furlong, EEM., Lawrence, ND. and Rattray, M., 2010. Model-based Method for Transcription Factor Target Identification with Limited Data Proc. Natl. Acad. Sci. USA, v. 107
Doi: http://doi.org/10.1073/pnas.0914285107
Honkela, A., Girardot, C., Gustafson, EH., Liu, Y-H., Furlong, EEM., Lawrence, ND. and Rattray, M., 2010. Model-based Method for Transcription Factor Target Identification with Limited Data Proc. Natl. Acad. Sci. USA, v. 107
Doi: http://doi.org/10.1073/pnas.0914285107
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
Asif, HMS., Rolfe, MD., Green, J., Lawrence, ND., Rattray, M. and Sanguinetti, G., 2010. TFInfer: a tool for probabilistic inference of transcription factor activities Bioinformatics, v. 26
Doi: http://doi.org/10.1093/bioinformatics/btq469
Asif, HMS., Rolfe, MD., Green, J., Lawrence, ND., Rattray, M. and Sanguinetti, G., 2010. TFInfer: a tool for probabilistic inference of transcription factor activities Bioinformatics, v. 26
Doi: http://doi.org/10.1093/bioinformatics/btq469
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
Asif, HMS., Rolfe, MD., Green, J., Lawrence, ND., Rattray, M. and Sanguinetti, G., 2010. TFInfer: a tool for probabilistic inference of transcription factor activities Bioinformatics, v. 26
Doi: http://doi.org/10.1093/bioinformatics/btq469
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
Asif, HMS., Rolfe, MD., Green, J., Lawrence, ND., Rattray, M. and Sanguinetti, G., 2010. TFInfer: a tool for probabilistic inference of transcription factor activities Bioinformatics, v. 26
Doi: http://doi.org/10.1093/bioinformatics/btq469
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
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
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
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., 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
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
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., 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
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
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
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., 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
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
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
Lawrence, ND., 2005. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models Journal of Machine Learning Research, v. 6
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
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
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
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
Lawrence, ND., Living Together: Mind and Machine Intelligence
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., 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
Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian
Processes Journal of Machine Learning Research, v. 12 (2011)
Lawrence, ND., Living Together: Mind and Machine Intelligence
Lawrence, ND., Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
Lawrence, ND., Data Readiness Levels
Fusi, N., Stegle, O. and Lawrence, N., Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects Nature Precedings,
Doi: http://doi.org/10.1038/npre.2011.5995.1
Lawrence, ND., Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
Paleyes, A., Urma, R-G. and Lawrence, ND., Challenges in Deploying Machine Learning: a Survey of Case Studies ACM Computing Surveys,
Delacroix, S. and Lawrence, N., Disturbing the ‘One Size Fits All’, Feudal Approach to Data Governance: Bottom-Up Data Trusts SSRN Electronic Journal,
Doi: http://doi.org/10.2139/ssrn.3265315
Lawrence, ND., Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
Vargas, F., Thodoroff, P., Lamacraft, A. and Lawrence, N., Solving Schrödinger Bridges via Maximum Likelihood Entropy (Basel), v. 23
Doi: 10.3390/e23091134
Lawrence, ND., Data Readiness Levels
Lawrence, ND., Data Readiness Levels
Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian
Processes Journal of Machine Learning Research, v. 12 (2011)
Lawrence, ND., Data Readiness Levels
Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian
Processes Journal of Machine Learning Research, v. 12 (2011)
Lawrence, ND., Data Readiness Levels
Álvarez, MA. and Lawrence, ND., Computationally Efficient Convolved Multiple Output Gaussian
Processes Journal of Machine Learning Research, v. 12 (2011)
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
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., 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
Hensman, J. and Lawrence, ND., Nested Variational Compression in Deep Gaussian Processes
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
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
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
Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
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
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
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
Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., Genetic Analysis of Transformed Phenotypes
Smith, MT., Zwiessele, M. and Lawrence, ND., Differentially Private Gaussian Processes
Damianou, A., Lawrence, ND. and Ek, CH., Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Smith, MT., Zwiessele, M. and Lawrence, ND., Differentially Private 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
Fusi, N., Lippert, C., Lawrence, ND. and Stegle, O., Genetic Analysis of Transformed Phenotypes
Internet publications
Paleyes, A., Urma, R-G. and Lawrence, ND., 2020. Challenges in Deploying Machine Learning: a Survey of Case Studies
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
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. 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
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. 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
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
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. 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., 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 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., 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., 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. 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
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
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., 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., 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. and Rattray, M., 2010. A Brief Introduction to Bayesian Inference
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
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. and Jordan, MI., 2006. Gaussian Processes and the Null-Category Noise Model
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
Software
Lawrence, ND., MOCAP Toolbox for MATLAB