| 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 | 215* | 2019 |
| Stein points WY Chen, L Mackey, J Gorham, FX Briol, CJ Oates International Conference on Machine Learning, PMLR, 844-853, 2018 | 107 | 2018 |
| Minimum Stein discrepancy estimators A Barp, FX Briol, AB Duncan, M Girolami, L Mackey Advances in Neural Information Processing Systems, 12964-12976, 2019 | 102 | 2019 |
| Frank-Wolfe Bayesian quadrature: Probabilistic integration with theoretical guarantees FX Briol, CJ Oates, M Girolami, MA Osborne Advanced in Neural Information Processing Systems, 1162-1170, 2015 | 89 | 2015 |
| Robust generalised Bayesian inference for intractable likelihoods T Matsubara, J Knoblauch, FX Briol, C Oates Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022 | 80 | 2022 |
| 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, 2022 | 78 | 2022 |
| 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 | 74* | 2019 |
| Statistical inference for generative models with maximum mean discrepancy FX Briol, A Barp, AB Duncan, M Girolami arXiv preprint arXiv:1906.05944, 2019 | 73 | 2019 |
| Convergence guarantees for Gaussian process means with misspecified likelihoods and smoothness G Wynne, FX Briol, M Girolami Journal of Machine Learning Research 22 (123), 1-40, 2021 | 67* | 2021 |
| 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 | 66 | 2019 |
| Bayesian quadrature for multiple related integrals X Xi, FX Briol, M Girolami International Conference on Machine Learning, PMLR 80, 5369-5378, 2018 | 48 | 2018 |
| Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap C Dellaporta, J Knoblauch, T Damoulas, FX Briol International Conference on Artificial Intelligence and Statistics, PMLR 151 …, 2022 | 39 | 2022 |
| 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 | 39 | 2018 |
| The ridgelet prior: A covariance function approach to prior specification for Bayesian neural networks T Matsubara, CJ Oates, FX Briol Journal of Machine Learning Research 22 (157), 1-57, 2021 | 24 | 2021 |
| On the sampling problem for kernel quadrature FX Briol, CJ Oates, J Cockayne, WY Chen, M Girolami International Conference on Machine Learning, PMLR 70, 586--595, 2017 | 22 | 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 | 20 | 2017 |
| Scalable control variates for Monte Carlo methods via stochastic optimization S Si, C Oates, AB Duncan, L Carin, FX Briol International Conference on Monte Carlo and Quasi-Monte Carlo Methods in …, 2020 | 18 | 2020 |
| Generalized Bayesian inference for discrete intractable likelihood T Matsubara, J Knoblauch, FX Briol, CJ Oates Journal of the American Statistical Association, 1-11, 2023 | 17 | 2023 |
| Robust and scalable Bayesian online changepoint detection M Altamirano, FX Briol, J Knoblauch International Conference on Machine Learning, PMLR 202, 642-663, 2023 | 16 | 2023 |
| ProbNum: Probabilistic numerics in Python J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ... arXiv preprint arXiv:2112.02100, 2021 | 16 | 2021 |
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
