| Bayesian neural network priors revisited V Fortuin, A Garriga-Alonso, SW Ober, F Wenzel, G Rätsch, RE Turner, ... arXiv preprint arXiv:2102.06571, 2021 | 148 | 2021 |
| The promises and pitfalls of deep kernel learning SW Ober, CE Rasmussen, M van der Wilk Uncertainty in Artificial Intelligence, 1206-1216, 2021 | 101 | 2021 |
| Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes SW Ober, L Aitchison International Conference on Machine Learning, 8248-8259, 2021 | 56 | 2021 |
| Benchmarking the Neural Linear Model for Regression SW Ober, CE Rasmussen arXiv preprint arXiv:1912.08416, 2019 | 47 | 2019 |
| Understanding Variational Inference in Function-Space DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk arXiv preprint arXiv:2011.09421, 2020 | 46 | 2020 |
| Deep kernel processes L Aitchison, A Yang, SW Ober International Conference on Machine Learning, 130-140, 2021 | 43 | 2021 |
| Modeling and detecting student attention and interest level using wearable computers Z Zhu, S Ober, R Jafari 2017 IEEE 14th international conference on wearable and implantable body …, 2017 | 33 | 2017 |
| Last Layer Marginal Likelihood for Invariance Learning P Schwöbel, M Jørgensen, SW Ober, M Van Der Wilk International Conference on Artificial Intelligence and Statistics, 3542-3555, 2022 | 23 | 2022 |
| Inducing point allocation for sparse gaussian processes in high-throughput bayesian optimisation HB Moss, SW Ober, V Picheny International Conference on Artificial Intelligence and Statistics, 5213-5230, 2023 | 16 | 2023 |
| 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 | 9 | 2023 |
| A variational approximate posterior for the deep Wishart process SW Ober, L Aitchison Thirty-Fifth Conference on Neural Information Processing Systems, 2021 | 6 | 2021 |
| An improved variational approximate posterior for the deep wishart process SW Ober, B Anson, E Milsom, L Aitchison Uncertainty in Artificial Intelligence, 1555-1563, 2023 | 5 | 2023 |
| Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation HB Moss, SW Ober, V Picheny arXiv preprint arXiv:2206.02437, 2022 | 4 | 2022 |
| Recommendations for Baselines and Benchmarking Approximate Gaussian Processes SW Ober, A Artemev, M Wagenländer, R Grobins, M van der Wilk arXiv preprint arXiv:2402.09849, 2024 | 1 | 2024 |
| Active learning for affinity prediction of antibodies A Gessner, SW Ober, O Vickery, D Oglić, T Uçar arXiv e-prints, arXiv: 2406.07263, 2024 | | 2024 |
| Towards Improved Variational Inference for Deep Bayesian Models SW Ober | | 2023 |
Mark van der WilkAssociate Professor, University of Oxford在 cs.ox.ac.uk 的电子邮件经过验证
