Items where authors include "Smith, M.T."

Article

Chapman, K.E. orcid.org/0000-0002-6584-6248, Smith, M.T., Gaston, K.J. orcid.org/0000-0002-7235-7928 et al. (1 more author) (2024) Bumblebee nest departures under low light conditions at sunrise and sunset. Biology Letters, 20 (4). ISSN 1744-957X

Smith, M.T., Ross, M., Ssematimba, J. et al. (3 more authors) (2023) Modelling calibration uncertainty in networks of environmental sensors. Journal of the Royal Statistical Society Series C: Applied Statistics. qlad075. ISSN 0035-9254

Smith, M.T., Grosse, K., Backes, M. et al. (1 more author) (2023) Adversarial vulnerability bounds for Gaussian process classification. Machine Learning, 112 (3). pp. 971-1009. ISSN 0885-6125

Smith, M.T., Livingstone, M. and Comont, R. (2021) A method for low‐cost, low‐impact insect tracking using retroreflective tags. Methods in Ecology and Evolution, 12 (11). pp. 2184-2195.

Ross, M., Smith, M.T. and Álvarez, M.A. (2021) Learning nonparametric Volterra kernels with Gaussian processes. arXiv. (Submitted)

Fotheringham, J., Smith, M.T., Froissart, M. et al. (5 more authors) (2020) Hospitalization and mortality following non-attendance for hemodialysis according to dialysis day of the week : a European cohort study. BMC Nephrology, 21 (1). 218.

Smith, M.T., Álvarez, M.A. and Lawrence, N.D. (2019) Differentially private regression and classification with sparse Gaussian processes. arXiv. (Submitted)

Smith, M.T., Álvarez, M.A. and Lawrence, N.D. orcid.org/0000-0001-9258-1030 (2019) Gaussian process regression for binned data. arXiv. (Submitted)

Smith, M.T., Zwiessele, M. and Lawrence, N.D. (2017) Differentially Private Gaussian Processes. arXiv. (Unpublished)

Berndt, S.I., Skibola, C.F., Joseph, V. et al. (127 more authors) (2013) Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia. Nature Genetics, 45 (8). pp. 868-876. ISSN 1061-4036

Lightfoot, T.J., Skibola, C.F., Willett, E.V. et al. (7 more authors) (2005) Risk of non-Hodgkin lymphoma associated with polymorphisms in folate-metabolizing genes. Cancer Epidemiology Biomarkers & Prevention, 14 (12). pp. 2999-3003. ISSN 1055-9965

Willett, E.V., Skibola, C.F., Adamson, P. et al. (4 more authors) (2005) Non-Hodgkin's Lymphoma: obesity and energy homeostasis polymorphisms. British Journal of Cancer, 93 (7). pp. 811-816. ISSN 0007-0920

Proceedings Paper

McDonald, T.M., Ross, M., Smith, M.T. et al. (1 more author) (2023) Nonparametric gaussian process covariances via multidimensional convolutions. In: Ruiz, F., Dy, J. and van de Meent, J-W, (eds.) Proceedings of Machine Learning Research. International Conference on Artificial Intelligence and Statistics, 25-27 Apr 2023, Palau de Congressos, Valencia, Spain. ML Research Press , pp. 8279-8293.

Gahungu, P., Lanyon, C.W., Álvarez, M.A. et al. (3 more authors) (2022) Adjoint-aided inference of Gaussian process driven differential equations. In: Advances in Neural Information Processing Systems (NeurIPS 2022). 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 28 Nov - 09 Dec 2022, New Orleans, LA, USA. . ISBN 9781713871088

Yousefi, F., Smith, M.T. and Alvarez Lopez, M. orcid.org/0000-0002-8980-4472 (2019) Multi-task Learning for aggregated data using Gaussian processes. In: Wallach , H., Larochelle, H. , Beygelzimer, A. , d'Alché-Buc, F., Fox, E. and Garnett, R. , (eds.) Proceedings of the conference on Advances in Neural Information Processing Systems (NIPS 2019). Advances in Neural Information Processing Systems (NeurIPS), 08-14 Dec 2019, Vancouver, Canada. Electronic Proceedings of the Neural Information Processing Systems Conference, 32 . Neural Information Processing Systems Foundation, Inc. .

Smith, M.T., Alvarez, M.A., Zwiessele, M. et al. (1 more author) (2018) Differentially private regression with Gaussian processes. In: Storkey, A. and Perez-Cruz, F., (eds.) Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics 2018, 09-11 Apr 2018, Lanzarote, Canary Islands. Proceedings of Machine Learning Research (84). PMLR , pp. 1195-1203.