| Adversarial examples, uncertainty, and transfer testing robustness in gaussian process hybrid deep networks J Bradshaw, AGG Matthews, Z Ghahramani arXiv preprint arXiv:1707.02476, 2017 | 180 | 2017 |
| A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in Neural Information Processing Systems 32, 2019 | 106 | 2019 |
| Are generative classifiers more robust to adversarial attacks? Y Li, J Bradshaw, Y Sharma International Conference on Machine Learning, 3804-3814, 2019 | 98 | 2019 |
| A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato arXiv preprint arXiv:1805.10970, 2018 | 72* | 2018 |
| Barking up the right tree: an approach to search over molecule synthesis dags J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in neural information processing systems 33, 6852-6866, 2020 | 56 | 2020 |
| Local latent space Bayesian optimization over structured inputs N Maus, H Jones, J Moore, MJ Kusner, J Bradshaw, J Gardner Advances in neural information processing systems 35, 34505-34518, 2022 | 46 | 2022 |
| Prefix-tree decoding for predicting mass spectra from molecules S Goldman, J Bradshaw, J Xin, C Coley Advances in Neural Information Processing Systems 36, 48548-48572, 2023 | 8 | 2023 |
| Generating molecules via chemical reactions J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato | 4 | 2019 |
| Machine Learning Methods for Modeling Synthesizable Molecules J Bradshaw | | 2021 |
