| BayesOpt Adversarial Attack R Binxin, C Adam, B Arno, Y Gal International Conference on Learning Representations, 2020 | 51* | 2020 |
| Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels B Ru, X Wan, X Dong, M Osborne ICLR 2021, 2020 | 49* | 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 | 47 | 2020 |
| Fast Information-theoretic Bayesian Optimisation B Ru, M McLeod, D Granziol, MA Osborne International Conference on Machine Learning (ICML) 2018, 2018 | 43 | 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 | 35 | 2019 |
| 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 | 22 | 2021 |
| Neural architecture generator optimization R Ru, P Esperanca, FM Carlucci Advances in Neural Information Processing Systems 33, 12057-12069, 2020 | 22 | 2020 |
| 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 | 17* | 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 | 15 | 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 | 13 | 2019 |
| 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 | 8 | 2021 |
| 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 | 3 | 2021 |
| 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 |
| On Redundancy and Diversity in Cell-based Neural Architecture Search X Wan, B Ru, PM Esperança, Z Li arXiv preprint arXiv:2203.08887, 2022 | 2 | 2022 |
| 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 | 2 | 2022 |
| Entropic spectral learning for large-scale graphs D Granziol, B Ru, S Zohren, X Dong, M Osborne, S Roberts arXiv preprint arXiv:1804.06802, 2018 | 2 | 2018 |
| Bayesian Generational Population-Based Training X Wan, C Lu, J Parker-Holder, PJ Ball, V Nguyen, B Ru, M Osborne First Conference on Automated Machine Learning (Main Track), 2022 | 1 | 2022 |
| DARTS without a Validation Set: Optimizing the Marginal Likelihood M Fil, B Ru, C Lyle, Y Gal arXiv preprint arXiv:2112.13023, 2021 | 1 | 2021 |
| 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 | 1 | 2021 |
| Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications D Granziol, B Ru, X Dong, S Zohren, M Osborne, S Roberts Algorithms 15 (6), 209, 2022 | | 2022 |
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
