| Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels B Ru*, X Wan*, X Dong, M Osborne International Conference on Learning Representations (ICLR) 9, 2021 | 92 | 2021 |
| Sentiment Correlation in Financial News Networks and Associated Market Movements X Wan*, J Yang*, S Marinov, JP Calliess, S Zohren, X Dong Scientific Reports 11 (3062), 2021 | 44 | 2021 |
| 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 International Conference on Machine Learning (ICML) 38, 10663-10674, 2021 | 36 | 2021 |
| Adversarial Attacks on Graph Classifiers via Bayesian Optimisation X Wan, H Kenlay, B Ru, A Blaas, M Osborne, X Dong Advances in Neural Information Processing Systems (NeurIPS) 34, 2021 | 21* | 2021 |
| Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization S Daulton, X Wan, D Eriksson, M Balandat, MA Osborne, E Bakshy Advances in Neural Information Processing Systems (NeurIPS) 35, 2022 | 20 | 2022 |
| Iterate Averaging in the Quest for Best Test Error D Granziol, N Baskerville, X Wan, S Albanie, S Roberts arXiv preprint arXiv:2003.01247, 2020 | 16* | 2020 |
| On Redundancy and Diversity in Cell-based Neural Architecture Search X Wan, B Ru, PM Esperança, Z Li International Conference on Learning Representations (ICLR) 10, 2022 | 15 | 2022 |
| Deep Curvature Suite D Granziol, X Wan, T Garipov arXiv preprint arXiv:1912.09656, 2019 | 15* | 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 (AutoML-Conf) 1, 14/1-27, 2022 | 13 | 2022 |
| BOiLS: Bayesian Optimisation for Logic Synthesis A Grosnit*, C Malherbe*, R Tutunov, X Wan, J Wang, HB Ammar Design, Automation & Test in Europe Conference (DATE) 25, 2022 | 13 | 2022 |
| Better Zero-Shot Reasoning with Self-Adaptive Prompting X Wan, R Sun, H Dai, SO Arik, T Pfister Findings of the Association for Computational Linguistics: ACL 2023, 2023 | 11 | 2023 |
| AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning H Zhou*, X Wan*, I Vulić, A Korhonen arXiv preprint arXiv:2301.12132, 2023 | 9* | 2023 |
| Approximate Neural Architecture Search via Operation Distribution Learning X Wan, B Ru, PM Esperança, FM Carlucci IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2377-2386, 2022 | 7 | 2022 |
| Universal Self-Adaptive Prompting X Wan, R Sun, H Nakhost, H Dai, JM Eisenschlos, SO Arik, T Pfister Empirical Methods in Natural Language Processing (EMNLP) (to appear), 2023 | 3 | 2023 |
| Explaining the Adaptive Generalisation Gap D Granziol, N Baskerville, X Wan, S Albanie, S Roberts arXiv preprint arXiv:2011.08181v4, 2020 | 3 | 2020 |
| Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning H Zhou*, X Wan*, I Vulić, A Korhonen Findings of the Association for Computational Linguistics: EMNLP 2023 (to …, 2023 | 1 | 2023 |
| Batch calibration: Rethinking calibration for in-context learning and prompt engineering H Zhou, X Wan, L Proleev, D Mincu, J Chen, K Heller, S Roy NeurIPS Workshop on Robustness of Zero/Few-shot Learning in Foundation …, 2023 | 1 | 2023 |
| Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature M Adachi*, S Hayakawa*, X Wan, M Jørgensen, H Oberhauser, ... arXiv preprint arXiv:2306.05843, 2023 | 1 | 2023 |
| SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning X Li, K Han, X Wan, VA Prisacariu arXiv preprint arXiv:2305.02385, 2023 | 1 | 2023 |
| Bayesian Optimisation of Functions on Graphs X Wan*, P Osselin*, H Kenlay, B Ru, MA Osborne, X Dong Advances in Neural Information Processing Systems (NeurIPS) (to appear) 36, 2023 | | 2023 |
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
