| Minimum stein discrepancy estimators A Barp, FX Briol, A Duncan, M Girolami, L Mackey Advances in Neural Information Processing Systems 32, 2019 | 106 | 2019 |
| Stein’s method meets computational statistics: A review of some recent developments A Anastasiou, A Barp, FX Briol, B Ebner, RE Gaunt, F Ghaderinezhad, ... Statistical Science 38 (1), 120-139, 2023 | 81 | 2023 |
| Statistical inference for generative models with maximum mean discrepancy FX Briol, A Barp, AB Duncan, M Girolami arXiv preprint arXiv:1906.05944, 2019 | 77 | 2019 |
| Stein point markov chain monte carlo WY Chen, A Barp, FX Briol, J Gorham, M Girolami, L Mackey, C Oates International Conference on Machine Learning, 1011-1021, 2019 | 68 | 2019 |
| Metrizing weak convergence with maximum mean discrepancies CJ Simon-Gabriel, A Barp, B Schölkopf, L Mackey Journal of Machine Learning Research 24 (184), 1-20, 2023 | 50 | 2023 |
| Geometry and dynamics for Markov chain Monte Carlo A Barp, FX Briol, AD Kennedy, M Girolami Annual Review of Statistics and Its Application 5 (1), 451-471, 2018 | 40 | 2018 |
| A Riemann–Stein kernel method A Barp, CJ Oates, E Porcu, M Girolami Bernoulli 28 (4), 2181-2208, 2022 | 30* | 2022 |
| Geometric methods for sampling, optimization, inference, and adaptive agents A Barp, L Da Costa, G França, K Friston, M Girolami, MI Jordan, ... Handbook of Statistics 46, 21-78, 2022 | 29* | 2022 |
| A Riemannian-Stein kernel method A Barp, C Oates, E Porcu, M Girolami arXiv preprint arXiv:1810.04946 1 (5), 6-9, 2018 | 19 | 2018 |
| A unifying and canonical description of measure-preserving diffusions A Barp, S Takao, M Betancourt, A Arnaudon, M Girolami arXiv preprint arXiv:2105.02845, 2021 | 18 | 2021 |
| A numerical study of the 3D random interchange and random loop models A Barp, EG Barp, FX Briol, D Ueltschi Journal of Physics A: Mathematical and Theoretical 48 (34), 345002, 2015 | 15 | 2015 |
| Targeted separation and convergence with kernel discrepancies A Barp, CJ Simon-Gabriel, M Girolami, L Mackey | 14 | 2022 |
| Hamiltonian Monte Carlo on symmetric and homogeneous spaces via symplectic reduction A Barp, A Kennedy, M Girolami arXiv preprint arXiv:1903.02699, 2019 | 14 | 2019 |
| Vector-valued control variates Z Sun, A Barp, FX Briol International Conference on Machine Learning, 32819-32846, 2023 | 13 | 2023 |
| Optimization on manifolds: A symplectic approach G França, A Barp, M Girolami, MI Jordan arXiv preprint arXiv:2107.11231, 2021 | 12 | 2021 |
| Controlling moments with kernel Stein discrepancies H Kanagawa, A Barp, A Gretton, L Mackey arXiv preprint arXiv:2211.05408, 2022 | 8 | 2022 |
| The bracket geometry of statistics AA Barp Imperial College London, 2020 | 8 | 2020 |
| Irreversible Langevin MCMC on lie groups A Arnaudon, A Barp, S Takao Geometric Science of Information: 4th International Conference, GSI 2019 …, 2019 | 8 | 2019 |
| Hamiltonian Monte Carlo on Lie groups and constrained mechanics on homogeneous manifolds A Barp Geometric Science of Information: 4th International Conference, GSI 2019 …, 2019 | 8 | 2019 |
| Posterior integration on a Riemannian manifold C Oates, A Barp, M Girolami arXiv preprint arXiv:1712.01793, 2017 | 3 | 2017 |
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
