Dr Michael Smith

Journal articles

• Chapman KE, Smith MT, Gaston KJ & Hempel de Ibarra N (2024) Bumblebee nest departures under low light conditions at sunrise and sunset. Biology Letters, 20(4).
• Smith MT, Grosse K, Backes M & Álvarez MA (2022) Adversarial vulnerability bounds for Gaussian process classification. Machine Learning. View this article in WRRO
• Smith MT, Livingstone M & Comont R (2021) A method for low‐cost, low‐impact insect tracking using retroreflective tags. Methods in Ecology and Evolution.
• Ross M, Smith MT & Álvarez MA (2021) Learning Nonparametric Volterra Kernels with Gaussian Processes. Advances in Neural Information Processing Systems, 29, 24099-24110.
• Smith MT, Alvarez MA & Lawrence ND (2021) Differentially Private Regression and Classification with Sparse Gaussian Processes. JOURNAL OF MACHINE LEARNING RESEARCH, 22.
• Fotheringham J, Smith MT, Froissart M, Kronenberg F, Stenvinkel P, Floege J, Eckardt K-U & Wheeler DC (2020) Hospitalization and mortality following non-attendance for hemodialysis according to dialysis day of the week : a European cohort study. BMC Nephrology, 21(1).
• Smith MT, Ssematimba J, Álvarez MA & Bainomugisha E (2019) Machine Learning for a Low-cost Air Pollution Network.. CoRR, abs/1911.12868.
• Smith MT, Álvarez MA & Lawrence ND (2019) Differentially Private Regression and Classification with Sparse Gaussian Processes.. CoRR, abs/1909.09147. View this article in WRRO
• Smith MT, Álvarez MA & Lawrence ND (2018) Gaussian Process Regression for Binned Data.. CoRR, abs/1809.02010. View this article in WRRO
• Smith MT, Zwiessele M & Lawrence ND (2016) Differentially Private Gaussian Processes.. CoRR, abs/1606.00720.
• Smith M, Ross M, Ssematimba J, Álvarez MA, Bainomugisha E & Wilkinson R () Modelling calibration uncertainty in networks of environmental sensors. Journal of the Royal Statistical Society Series C: Applied Statistics.

Conference proceedings papers

• McDonald TM, Ross M, Smith MT & Álvarez MA (2023) Nonparametric Gaussian Process Covariances via Multidimensional Convolutions. Proceedings of Machine Learning Research, Vol. 206 (pp 8279-8293)
• Gahungu P, Christopher L, Alvarez M, Bainomugisha E, Smith M & Wilkinson R (2022) Adjoint-aided inference of Gaussian process driven differential equations. Conference on Neural Information Processing Systems, 29 November 2022 - 1 December 2022.
• Grosse K, Smith MT & Backes M (2021) Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks. 2020 25th International Conference on Pattern Recognition (ICPR), 10 January 2021 - 15 January 2021.
• Yousefi F, Smith MT & Álvarez MA (2019) Multi-task Learning for Aggregated Data using Gaussian Processes.. NeurIPS (pp 15050-15060)
• Yousefi F, Smith MT & Alvarez Lopez M () Multi-task Learning for Aggregated Data using Gaussian Processes. Proceedings of the Conference on Advances in Neural Information Processing Systems 32 (2019) View this article in WRRO
• Smith M, Alvarez Lopez MA, Zwiessele M & Lawrence N () Differentially Private Regression with Gaussian processes. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Vol. 84 (pp 1195-1203). Playa Blanca, Lanzarote, Canary Islands, 9 April 2018 - 11 April 2018. View this article in WRRO

Working papers

• Smith MT, Zwiessele M & Lawrence ND () Differentially Private Gaussian Processes. View this article in WRRO

Preprints

• Ross M, Smith MT & Álvarez MA (2021) Learning Nonparametric Volterra Kernels with Gaussian Processes, arXiv.
• Smith MT, Ssematimba J, Alvarez MA & Bainomugisha E (2019) Machine Learning for a Low-cost Air Pollution Network, arXiv.
• Smith MT, Grosse K, Backes M & Alvarez MA (2019) Adversarial Vulnerability Bounds for Gaussian Process Classification, arXiv. View this article in WRRO
• Smith MT, Alvarez MA & Lawrence ND (2019) Differentially Private Regression and Classification with Sparse Gaussian Processes, arXiv.
• Yousefi F, Smith MT & Álvarez MA (2019) Multi-task Learning for Aggregated Data using Gaussian Processes, arXiv.
• Smith MT, Alvarez MA & Lawrence ND (2018) Gaussian Process Regression for Binned Data, arXiv.