| Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants M Willetts, S Hollowell, L Aslett, C Holmes, A Doherty Scientific reports 8 (1), 7961, 2018 | 208 | 2018 |
| Explicit Regularisation in Gaussian Noise Injections A Camuto, M Willetts, U Şimşekli, S Roberts, C Holmes Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020 | 61 | 2020 |
| Improving VAEs’ Robustness to Adversarial Attack M Willetts, A Camuto, T Rainforth, S Roberts, C Holmes International Conference on Learning Representations (ICLR) 2021, 2021 | 31 | 2021 |
| Multi-Facet Clustering Variational Autoencoders F Falck, H Zhang, M Willetts, G Nicholson, C Yau, CC Holmes Advances in Neural Information Processing Systems (NeurIPS) 2021, 2021 | 30 | 2021 |
| Towards a Theoretical Understanding of the Robustness of Variational Autoencoders A Camuto, M Willetts, S Roberts, C Holmes, T Rainforth International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 | 29 | 2021 |
| I Don't Need u: Identifiable Non-Linear ICA Without Side Information M Willetts, B Paige arXiv preprint arXiv:2106.05238, 2021 | 17 | 2021 |
| Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels M Willetts, SJ Roberts, CC Holmes IEEE International Conference on Big Data 2020 -- Machine Learning on Big Data, 2020 | 17* | 2020 |
| Certifiably Robust Variational Autoencoders B Barrett, A Camuto, M Willetts, T Rainforth International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 | 16 | 2022 |
| Non-determinism in tensorflow resnets M Morin, M Willetts arXiv preprint arXiv:2001.11396, 2020 | 14 | 2020 |
| Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders M Willetts, S Roberts, C Holmes NeurIPS 2019 Workshop on Bayesian Deep Learning, 2019 | 12 | 2019 |
| A multi-resolution framework for U-Nets with applications to hierarchical VAEs F Falck, C Williams, D Danks, G Deligiannidis, C Yau, CC Holmes, ... Advances in Neural Information Processing Systems 35, 15529-15544, 2022 | 6 | 2022 |
| Relaxed-Responsibility Hierarchical Discrete VAEs M Willetts, X Miscouridou, S Roberts, C Holmes NeurIPS 2021 Workshop on Bayesian Deep Learning, 2020 | 4 | 2020 |
| Semi-unsupervised Learning of Human Activity using Deep Generative Models M Willetts, A Doherty, S Roberts, C Holmes NeurIPS 2018 ML4Health Workshop, 2018 | 3 | 2018 |
| Semi-unsupervised Learning using Deep Generative Models M Willetts, A Doherty, S Roberts, C Holmes NeurIPS 2018 Workshop on Bayesian Deep Learning, 2018 | 3 | 2018 |
| Learning Bijective Feature Maps for Linear ICA A Camuto, M Willetts, B Paige, C Holmes, S Roberts International Conference on Artificial Intelligence and Statistics (AISTATS), 2021 | 2 | 2021 |
| Variational Autoencoders: A Harmonic Perspective A Camuto, M Willetts International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 | 1 | 2022 |
| Multiblock MEV opportunities & protections in dynamic AMMs M Willetts, C Harrington arXiv. org, 2024 | | 2024 |
| Optimal Rebalancing in Dynamic AMMs M Willetts, C Harrington arXiv preprint arXiv:2403.18737, 2024 | | 2024 |
| Closed-form solutions for generic N-token AMM arbitrage M Willetts, C Harrington arXiv preprint arXiv:2402.06731, 2024 | | 2024 |
| Robustness, structure and hierarchy in deep generative models MJF Willetts University of Oxford, 2021 | | 2021 |
Chris HolmesVerified email at stats.ox.ac.uk
