| On feature collapse and deep kernel learning for single forward pass uncertainty J van Amersfoort, L Smith, A Jesson, O Key, Y Gal arXiv preprint arXiv:2102.11409, 2021 | 170* | 2021 |
| Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties L Schut, O Key, R Mc Grath, L Costabello, B Sacaleanu, Y Gal International Conference on Artificial Intelligence and Statistics, 1756-1764, 2021 | 58 | 2021 |
| No train no gain: Revisiting efficient training algorithms for transformer-based language models J Kaddour, O Key, P Nawrot, P Minervini, MJ Kusner Advances in Neural Information Processing Systems 36, 25793-25818, 2023 | 35 | 2023 |
| Interlocking Backpropagation: Improving depthwise model-parallelism AN Gomez, O Key, K Perlin, S Gou, N Frosst, J Dean, Y Gal Journal of Machine Learning Research 23 (171), 1-28, 2022 | 25 | 2022 |
| Composite goodness-of-fit tests with kernels O Key, A Gretton, FX Briol, T Fernandez arXiv preprint arXiv:2111.10275, 2021 | 19 | 2021 |
| Towards Healing the Blindness of Score Matching M Zhang, O Key, P Hayes, D Barber, B Paige, FX Briol arXiv preprint arXiv:2209.07396, 2022 | 16 | 2022 |
| Optimally-weighted estimators of the maximum mean discrepancy for likelihood-free inference A Bharti, M Naslidnyk, O Key, S Kaski, FX Briol International Conference on Machine Learning, 2289-2312, 2023 | 13 | 2023 |
| On signal-to-noise ratio issues in variational inference for deep Gaussian processes TGJ Rudner, O Key, Y Gal, T Rainforth International Conference on Machine Learning, 9148-9156, 2021 | 4 | 2021 |
| Scalable data assimilation with message passing O Key, S Takao, D Giles, MP Deisenroth Environmental Data Science 4, e1, 2025 | | 2025 |
| Approximate Top- for Increased Parallelism O Key, L Ribar, A Cattaneo, L Hudlass-Galley, D Orr arXiv preprint arXiv:2412.04358, 2024 | | 2024 |
