| How powerful are performance predictors in neural architecture search? C White, A Zela, R Ru, Y Liu, F Hutter Advances in Neural Information Processing Systems 34, 28454-28469, 2021 | 89 | 2021 |
| Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels B Ru, X Wan, X Dong, M Osborne ICLR 2021, 2020 | 83 | 2020 |
| BayesOpt Adversarial Attack R Binxin, C Adam, B Arno, Y Gal International Conference on Learning Representations, 2020 | 75* | 2020 |
| Bayesian optimisation over multiple continuous and categorical inputs B Ru, A Alvi, V Nguyen, MA Osborne, S Roberts International Conference on Machine Learning, 8276-8285, 2020 | 68 | 2020 |
| Fast Information-theoretic Bayesian Optimisation B Ru, M McLeod, D Granziol, MA Osborne International Conference on Machine Learning (ICML) 2018, 2018 | 50 | 2018 |
| Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation AS Alvi, B Ru, J Calliess, SJ Roberts, MA Osborne International Conference on Machine Learning (ICML) 2019, 2019 | 44 | 2019 |
| Speedy Performance Estimation for Neural Architecture Search B Ru, C Lyle, L Schut, M van der Wilk, Y Gal Advances in Neural Information Processing Systems, 2021, 2021 | 37* | 2021 |
| Neural architecture generator optimization R Ru, P Esperanca, FM Carlucci Advances in Neural Information Processing Systems 33, 12057-12069, 2020 | 35 | 2020 |
| Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces X Wan, V Nguyen, H Ha, B Ru, C Lu, MA Osborne arXiv preprint arXiv:2102.07188, 2021 | 32 | 2021 |
| A bayesian perspective on training speed and model selection C Lyle, L Schut, R Ru, Y Gal, M van der Wilk Advances in neural information processing systems 33, 10396-10408, 2020 | 21 | 2020 |
| MEMe: An accurate maximum entropy method for efficient approximations in large-scale machine learning D Granziol, B Ru, S Zohren, X Dong, M Osborne, S Roberts Entropy 21 (6), 551, 2019 | 16 | 2019 |
| Bayesian generational population-based training X Wan, C Lu, J Parker-Holder, PJ Ball, V Nguyen, B Ru, M Osborne International Conference on Automated Machine Learning, 14/1-27, 2022 | 12 | 2022 |
| On redundancy and diversity in cell-based neural architecture search X Wan, B Ru, PM Esperança, Z Li arXiv preprint arXiv:2203.08887, 2022 | 11 | 2022 |
| Adversarial attacks on graph classifiers via bayesian optimisation X Wan, H Kenlay, R Ru, A Blaas, MA Osborne, X Dong Advances in Neural Information Processing Systems 34, 6983-6996, 2021 | 11 | 2021 |
| Neural architecture search: Insights from 1000 papers C White, M Safari, R Sukthanker, B Ru, T Elsken, A Zela, D Dey, F Hutter arXiv preprint arXiv:2301.08727, 2023 | 9 | 2023 |
| Learning to identify top elo ratings: A dueling bandits approach X Yan, Y Du, B Ru, J Wang, H Zhang, X Chen Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8797-8805, 2022 | 6 | 2022 |
| DHA: End-to-end joint optimization of data augmentation policy, hyper-parameter and architecture K Zhou, L Hong, S Hu, F Zhou, B Ru, J Feng, Z Li arXiv preprint arXiv:2109.05765, 2021 | 6 | 2021 |
| Approximate neural architecture search via operation distribution learning X Wan, B Ru, PM Esparança, FM Carlucci Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 4 | 2022 |
| VBALD-Variational Bayesian approximation of log determinants D Granziol, E Wagstaff, BX Ru, M Osborne, S Roberts arXiv preprint arXiv:1802.08054, 2018 | 3 | 2018 |
| DARTS without a validation set: Optimizing the marginal likelihood M Fil, B Ru, C Lyle, Y Gal arXiv preprint arXiv:2112.13023, 2021 | 2 | 2021 |
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
