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François-Xavier Briol
François-Xavier Briol
Lecturer in Statistical Science, UCL
Verified email at turing.ac.uk - Homepage
Title
Cited by
Cited by
Year
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
157*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
742015
Stein points
WY Chen, L Mackey, J Gorham, FX Briol, CJ Oates
Proceedings of the 35th International Conference on Machine Learning, 844-853, 2018
722018
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
51*2019
Minimum Stein discrepancy estimators
A Barp, FX Briol, AB Duncan, M Girolami, L Mackey
Advances in Neural Information Processing Systems, 12964-12976, 2019
452019
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
372019
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
372018
Bayesian quadrature for multiple related integrals
X Xi, FX Briol, M Girolami
Proceedings of the 35th International Conference on Machine Learning 80 …, 2018
342018
Statistical inference for generative models with maximum mean discrepancy
FX Briol, A Barp, AB Duncan, M Girolami
arXiv preprint arXiv:1906.05944, 2019
282019
Convergence guarantees for Gaussian process means with misspecified likelihoods and smoothness
G Wynne, FX Briol, M Girolami
Journal of Machine Learning Research 22, 2021
21*2021
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
192017
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
142017
Stein's method meets Statistics: A review of some recent developments
A Anastasiou, A Barp, FX Briol, B Ebner, RE Gaunt, F Ghaderinezhad, ...
arXiv preprint arXiv:2105.03481, 2021
132021
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
122015
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
112021
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
82020
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 …, 2021
72021
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
5*2016
Composite goodness-of-fit tests with kernels
O Key, T Fernandez, A Gretton, FX Briol
arXiv preprint arXiv:2111.10275, 2021
32021
A general method for calibrating stochastic radio channel models with kernels
A Bharti, FX Briol, T Pedersen
IEEE Transactions on Antennas and Propagation, 2021
32021
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