| 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 | 78 | 2020 |
| Bayesian image classification with deep convolutional Gaussian processes V Dutordoir, M Wilk, A Artemev, J Hensman International Conference on Artificial Intelligence and Statistics, 1529-1539, 2020 | 33* | 2020 |
| Scalable Thompson sampling using sparse Gaussian process models S Vakili, H Moss, A Artemev, V Dutordoir, V Picheny Advances in neural information processing systems 34, 5631-5643, 2021 | 30 | 2021 |
| Doubly Sparse Variational Gaussian Processes V Adam, S Eleftheriadis, N Durrande, A Artemev, J Hensman The 23rd International Conference on Artificial Intelligence and Statistics, 2020 | 19 | 2020 |
| GPflux: A library for deep Gaussian processes V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ... arXiv preprint arXiv:2104.05674, 2021 | 17 | 2021 |
| Tighter bounds on the log marginal likelihood of Gaussian process regression using conjugate gradients A Artemev, DR Burt, M van der Wilk International Conference on Machine Learning, 362-372, 2021 | 9 | 2021 |
| Variational Gaussian Process Models without Matrix Inverses M van der Wilk, ST John, A Artemev, J Hensman 2nd Symposium on Advances in Approximate Bayesian Inference, 2019 | 6 | 2019 |
| Ordinal bayesian optimisation V Picheny, S Vakili, A Artemev arXiv preprint arXiv:1912.02493, 2019 | 5 | 2019 |
| Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5′ to 3′ degradation L Forbes Beadle, JC Love, Y Shapovalova, A Artemev, M Rattray, ... PLoS biology 21 (1), e3001956, 2023 | 2 | 2023 |
| Modelling global mRNA dynamics during Drosophila embryogenesis reveals a relationship between mRNA degradation and P-bodies LF Beadle, JC Love, Y Shapovalova, A Artemev, M Rattray, HL Ashe bioRxiv, 2022.03. 17.484585, 2022 | 2 | 2022 |
| Improved inverse-free variational bounds for sparse Gaussian processes M van der Wilk, A Artemev, J Hensman Fourth Symposium on Advances in Approximate Bayesian Inference, 2022 | 2 | 2022 |
| Barely biased learning for Gaussian process regression DR Burt, A Artemev, M van der Wilk arXiv preprint arXiv:2109.09417, 2021 | 2 | 2021 |
| Memory safe computations with XLA compiler A Artemev, Y An, T Roeder, M van der Wilk Advances in Neural Information Processing Systems 35, 18970-18982, 2022 | 1 | 2022 |
| Automatic tuning of stochastic gradient descent with bayesian optimisation V Picheny, V Dutordoir, A Artemev, N Durrande Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 1 | 2021 |
| Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow V Picheny, J Berkeley, HB Moss, H Stojic, U Granta, SW Ober, A Artemev, ... arXiv preprint arXiv:2302.08436, 2023 | | 2023 |
| Efficient computational inference V Adam, S Eleftheriadis, N Durrande, A Artemev, J Hensman, L Bordeaux US Patent App. 17/753,723, 2022 | | 2022 |
| Computational implementation of gaussian process models M VAN DER WILK, S John, A Artemev, J Hensman US Patent 11,475,279, 2022 | | 2022 |
| Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees A Terenin, DR Burt, A Artemev, S Flaxman, M van der Wilk, ... arXiv preprint arXiv:2210.07893, 2022 | | 2022 |
| Single molecule imaging and modelling of mRNA decay dynamics in the Drosophila embryo LF Beadle, JC Love, Y Shapovalova, A Artemev, M Rattray, HL Ashe | | 2022 |
| Placement Project: Sparse State inference for GPSSM and SDE latent models V Adam, A Artemev | | 2019 |
Mark van der WilkImperial College LondonVerified email at imperial.ac.uk
