| Differentially private regression with Gaussian processes MT Smith, MA Álvarez, M Zwiessele, ND Lawrence International Conference on Artificial Intelligence and Statistics, 1195-1203, 2018 | 56* | 2018 |
| The limitations of model uncertainty in adversarial settings K Grosse, D Pfaff, MT Smith, M Backes arXiv preprint arXiv:1812.02606, 2018 | 44 | 2018 |
| The postsubiculum and spatial learning: the role of postsubicular synaptic activity and synaptic plasticity in hippocampal place cell, object, and object-location memory D Bett, CH Stevenson, KL Shires, MT Smith, SJ Martin, PA Dudchenko, ... Journal of Neuroscience 33 (16), 6928-6943, 2013 | 32 | 2013 |
| Multi-task learning for aggregated data using Gaussian processes F Yousefi, MT Smith, M Alvarez Advances in Neural Information Processing Systems 32, 2019 | 31 | 2019 |
| Physiological signal variability in hMT+ reflects performance on a direction discrimination task MG Wutte, MT Smith, VL Flanagin, T Wolbers Frontiers in psychology 2, 9664, 2011 | 27 | 2011 |
| Hospitalization and mortality following non-attendance for hemodialysis according to dialysis day of the week: a European cohort study J Fotheringham, MT Smith, M Froissart, F Kronenberg, P Stenvinkel, ... BMC nephrology 21, 1-10, 2020 | 12 | 2020 |
| Killing four birds with one Gaussian process: The relation between different test-time attacks K Grosse, MT Smith, M Backes 2020 25th International Conference on Pattern Recognition (ICPR), 4696-4703, 2021 | 11* | 2021 |
| Gaussian process regression for binned data MT Smith, MA Alvarez, ND Lawrence arXiv preprint arXiv:1809.02010, 2018 | 11* | 2018 |
| Adversarial vulnerability bounds for Gaussian process classification MT Smith, K Grosse, M Backes, MA Alvarez Machine Learning 112 (3), 971-1009, 2023 | 9 | 2023 |
| How wrong am I?-Studying adversarial examples and their impact on uncertainty in Gaussian process machine learning models K Grosse, D Pfaff, MT Smith, M Backes arXiv preprint arXiv:1711.06598, 2017 | 8 | 2017 |
| Learning nonparametric Volterra kernels with Gaussian processes M Ross, MT Smith, M Álvarez Advances in Neural Information Processing Systems 34, 24099-24110, 2021 | 7 | 2021 |
| A method for low‐cost, low‐impact insect tracking using retroreflective tags MT Smith, M Livingstone, R Comont Methods in Ecology and Evolution 12 (11), 2184-2195, 2021 | 7 | 2021 |
| Adjoint-aided inference of Gaussian process driven differential equations P Gahungu, C Lanyon, MA Álvarez, E Bainomugisha, MT Smith, ... Advances in Neural Information Processing Systems 35, 17233-17247, 2022 | 5 | 2022 |
| Fluctuations in the open time of synaptic channels: an application to noise analysis based on charge H Feldwisch-Drentrup, AB Barrett, MT Smith, MCW van Rossum Journal of neuroscience methods 210 (1), 15-21, 2012 | 5 | 2012 |
| Malaria surveillance with multiple data sources using Gaussian process models M Mubangizi, R Andrade-Pacheco, M Smith, JA Quinn, N Lawrence 1st International Conference on the Use of Mobile ICT in Africa, 2014 | 4 | 2014 |
| Differentially private regression and classification with sparse gaussian processes MT Smith, MA Álvarez, ND Lawrence Journal of Machine Learning Research 22 (188), 1-41, 2021 | 3 | 2021 |
| Machine Learning for a Low-cost Air Pollution Network MT Smith, J Ssematimba, MA Álvarez, E Bainomugisha arXiv preprint arXiv:1911.12868, 2019 | 2 | 2019 |
| Modelling calibration uncertainty in networks of environmental sensors MT Smith, M Ross, J Ssematimba, MA Álvarez, E Bainomugisha, ... Journal of the Royal Statistical Society Series C: Applied Statistics 72 (5 …, 2023 | 1 | 2023 |
| Nonparametric Gaussian process covariances via multidimensional convolutions TM McDonald, M Ross, MT Smith, MA Álvarez International Conference on Artificial Intelligence and Statistics, 8279-8293, 2023 | 1 | 2023 |
| Shallow and deep nonparametric convolutions for Gaussian processes TM McDonald, M Ross, MT Smith, MA Álvarez arXiv preprint arXiv:2206.08972, 2022 | 1 | 2022 |
