| Explainability for fair machine learning T Begley, T Schwedes, C Frye, I Feige arXiv preprint arXiv:2010.07389, 2020 | 49 | 2020 |
| Mesh dependence in PDE-constrained optimisation T Schwedes, DA Ham, SW Funke, MD Piggott, T Schwedes, DA Ham, ... Mesh Dependence in PDE-Constrained Optimisation: An Application in Tidal …, 2017 | 42* | 2017 |
| An iteration count estimate for a mesh-dependent steepest descent method based on finite elements and Riesz inner product representation T Schwedes, SW Funke, DA Ham arXiv preprint arXiv:1606.08069, 2016 | 14 | 2016 |
| Rao-blackwellised parallel mcmc T Schwedes, B Calderhead International Conference on Artificial Intelligence and Statistics, 3448-3456, 2021 | 8 | 2021 |
| Quasi markov chain monte carlo methods T Schwedes, B Calderhead arXiv preprint arXiv:1807.00070, 2018 | 6 | 2018 |
| Task-specific experimental design for treatment effect estimation B Connolly, K Moore, T Schwedes, A Adam, G Willis, I Feige, C Frye International Conference on Machine Learning, 6384-6401, 2023 | 3 | 2023 |
| Parallel Markov Chain Quasi-Monte Carlo Methods T Schwedes Imperial College London, 2019 | 1 | 2019 |
| Consistency of Markov chain quasi Monte Carlo with multiple proposals T Schwedes, B Calderhead | | |
| Discretisation sensitivity in gradient-based optimisation methods on continuous Hilbert spaces T Schwedes | | |
David HamProfessor of Computational Mathematics, Imperial College London在 imperial.ac.uk 的电子邮件经过验证
