| Many-body coarse-grained interactions using Gaussian approximation potentials ST John, G Csányi The Journal of Physical Chemistry B 121 (48), 10934-10949, 2017 | 124 | 2017 |
| A framework for interdomain and multioutput Gaussian processes M van der Wilk, V Dutordoir, ST John, A Artemev, V Adam, J Hensman arXiv preprint arXiv:2003.01115, 2020 | 100 | 2020 |
| Learning invariances using the marginal likelihood M van der Wilk, M Bauer, ST John, J Hensman Proceedings of the 32nd International Conference on Neural Information …, 2018 | 88 | 2018 |
| A tutorial on sparse Gaussian processes and variational inference F Leibfried, V Dutordoir, ST John, N Durrande arXiv preprint arXiv:2012.13962, 2020 | 48 | 2020 |
| Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments N BinTayyash, S Georgaka, ST John, S Ahmed, A Boukouvalas, ... Bioinformatics 37 (21), 3788-3795, 2021 | 37 | 2021 |
| Large-scale Cox process inference using variational Fourier features ST John, J Hensman International Conference on Machine Learning, 2362-2370, 2018 | 36 | 2018 |
| Machine learning system A Tukiainen, D Kim, T Nicholson, M Tomczak, JEMDEC FLORES, ... US Patent App. 16/753,580, 2020 | 28 | 2020 |
| GPflux: A Library for Deep Gaussian Processes V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ... PROBPROG2021, arXiv:2104.05674, 2021 | 27 | 2021 |
| Spectroscopic method to measure the superfluid fraction of an ultracold atomic gas ST John, Z Hadzibabic, NR Cooper Physical Review A—Atomic, Molecular, and Optical Physics 83 (2), 023610, 2011 | 22 | 2011 |
| Queer In AI: A Case Study in Community-Led Participatory AI O Queer in AI, A Ovalle, A Subramonian, A Singh, C Voelcker, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 18 | 2023 |
| Non-separable spatio-temporal graph kernels via SPDEs AV Nikitin, ST John, A Solin, S Kaski International Conference on Artificial Intelligence and Statistics, 10640-10660, 2022 | 17 | 2022 |
| Gaussian process modulated Cox processes under linear inequality constraints AF López-Lopera, ST John, N Durrande The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 17 | 2019 |
| Variational Gaussian process models without matrix inverses M van der Wilk, ST John, A Artemev, J Hensman Symposium on Advances in Approximate Bayesian Inference, 1-9, 2020 | 7 | 2020 |
| A tutorial on sparse Gaussian processes and variational inference. arXiv 2020 F Leibfried, V Dutordoir, S John, N Durrande arXiv preprint arXiv:2012.13962, 0 | 5 | |
| Causal modeling of policy interventions from treatment-outcome sequences Ç Hızlı, ST John, AT Juuti, TT Saarinen, KH Pietiläinen, P Marttinen International Conference on Machine Learning, 13050-13084, 2023 | 4 | 2023 |
| Memory-based dual Gaussian processes for sequential learning PE Chang, P Verma, ST John, A Solin, ME Khan International Conference on Machine Learning, 4035-4054, 2023 | 4 | 2023 |
| Fantasizing with dual GPs in Bayesian optimization and active learning PE Chang, P Verma, ST John, V Picheny, H Moss, A Solin arXiv preprint arXiv:2211.01053, 2022 | 4 | 2022 |
| Amortized variance reduction for doubly stochastic objective A Boustati, S Vakili, J Hensman, ST John Conference on Uncertainty in Artificial Intelligence, 61-70, 2020 | 4 | 2020 |
| Beyond Intuition, a Framework for Applying GPs to Real-World Data K Tazi, JA Lin, R Viljoen, A Gardner, ST John, H Ge, RE Turner arXiv preprint arXiv:2307.03093, 2023 | 3 | 2023 |
| Computational implementation of Gaussian process models M VAN DER WILK, ST John, A Artemev, J Hensman US Patent 11,475,279, 2022 | 3 | 2022 |
