| 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 | 123* | 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 | 45 | 2021 |
| 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 | 15 | 2022 |
| 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, 2024 | 14 | 2024 |
| Composite goodness-of-fit tests with kernels O Key, A Gretton, FX Briol, T Fernandez arXiv preprint arXiv:2111.10275, 2021 | 12 | 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 | 10 | 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 | 6 | 2023 |
| Local LoRA: Memory-Efficient Fine-Tuning of Large Language Models O Key, J Kaddour, P Minervini Workshop on Advancing Neural Network Training: Computational Efficiency …, 2023 | 1 | 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 | 1 | 2021 |
