| Probabilistic integration: A role in statistical computation? (with discussion) FX Briol, CJ Oates, M Girolami, M Osborne, D Sejdinovic Statistical Science 34 (1), 1-22, 2019 | 119* | 2019 |
| Frank-Wolfe Bayesian quadrature: Probabilistic integration with theoretical guarantees FX Briol, C Oates, M Girolami, MA Osborne Advances in Neural Information Processing Systems, 1162-1170, 2015 | 56 | 2015 |
| Stein Points WY Chen, L Mackey, J Gorham, FX Briol, CJ Oates Proceedings of the 35th International Conference on Machine Learning, 844-853, 2018 | 43 | 2018 |
| Convergence rates for a class of estimators based on Stein’s method CJ Oates, J Cockayne, FX Briol, M Girolami Bernoulli 25 (2), 1141-1159, 2019 | 40* | 2019 |
| Bayesian Quadrature for Multiple Related Integrals X Xi, FX Briol, M Girolami Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 24 | 2018 |
| 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), 2018 | 22 | 2018 |
| On the Sampling Problem for Kernel Quadrature FX Briol, CJ Oates, J Cockayne, WY Chen, M Girolami Proceedings of the 34th International Conference on Machine Learning 70, 586 …, 2017 | 15 | 2017 |
| Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models CJ Oates, S Niederer, A Lee, FX Briol, M Girolami Advances in Neural Information Processing Systems, 2017 | 14 | 2017 |
| 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 | 12 | 2015 |
| Minimum stein discrepancy estimators A Barp, FX Briol, A Duncan, M Girolami, L Mackey Advances in Neural Information Processing Systems, 12964-12976, 2019 | 10 | 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, PMLR 97, 1011-1021, 2019 | 9 | 2019 |
| Statistical inference for generative models with maximum mean discrepancy FX Briol, A Barp, AB Duncan, M Girolami arXiv preprint arXiv:1906.05944, 2019 | 7 | 2019 |
| Convergence guarantees for Gaussian process approximations under several observation models G Wynne, FX Briol, M Girolami arXiv preprint arXiv:2001.10818, 2020 | 3 | 2020 |
| Comments on" Bayesian Solution Uncertainty Quantification for Differential Equations" by Chkrebtii, Campbell, Calderhead & Girolami FX Briol, J Cockayne, O Teymur Bayesian Analysis 11 (4), 1285-1293, 2016 | 3* | 2016 |
| Rejoinder: Probabilistic integration: A role in statistical computation? FX Briol, CJ Oates, M Girolami, MA Osborne, D Sejdinovic Statistical Science 34 (1), 38-42, 2019 | 1 | 2019 |
| Bayesian numerical methods as a case study for statistical data science FX Briol, M Girolami Statistical Data Science, 99-110, 2018 | 1 | 2018 |
| Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization S Si, C Oates, AB Duncan, L Carin, FX Briol arXiv preprint arXiv:2006.07487, 2020 | | 2020 |
| Bayesian Probabilistic Numerical Integration with Tree-Based Models H Zhu, X Liu, R Kang, Z Shen, S Flaxman, FX Briol arXiv preprint arXiv:2006.05371, 2020 | | 2020 |
| Joint Modeling of Received Power, Mean Delay, and Delay Spread for Wideband Radio Channels A Bharti, R Adeogun, X Cai, W Fan, FX Briol, L Clavier, T Pedersen arXiv preprint arXiv:2005.06808, 2020 | | 2020 |
| Contributed Discussion of "A Bayesian Conjugate Gradient Method" FX Briol, FA Diaz de la O, PO Hristov Bayesian Analysis 14 (3), 980-984, 2019 | | 2019 |
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
