Publications

This page contains all my publications; for more details, see my Google Scholar profile.

Preprints

  • Briol, F-X., Barp, A., Duncan, A. B., Girolami, M. (2019). Statistical inference for generative models with maximum mean discrepancy. arXiv:1906.05944. (Preprint)

Published Papers

  • Barp, A., Briol, F-X., Duncan, A. B., Girolami, M., Mackey, L. (2019). Minimum Stein discrepancy estimators. arXiv:1906.08283. To appear in Neural Information Processing Systems. (Preprint) (Talk/Video)

  • Chen, W. Y., Barp, A., Briol, F-X., Gorham, J., Girolami, M., Mackey, L., Oates, C. J. (2019). Stein point Markov chain Monte Carlo. International Conference on Machine Learning, PMLR 97:1011-1021. (Conference) (Preprint)

  • Briol, F-X., Oates, C. J., Girolami, M., Osborne, M. A. & Sejdinovic, D. (2019). Probabilistic integration: a role in statistical computation? Statistical Science, Vol 34, Number 1, pp1-22. (Journal) (Preprint) (Supplement)
  • Oates, C. J., Cockayne, J., Briol, F-X. & Girolami, M. (2019). Convergence rates for a class of estimators based on Stein’s identity. Bernoulli, Vol. 25, No. 2, 1141-1159. (Journal) (Preprint)

  • Xi, X., Briol, F-X. & Girolami, M. (2018). Bayesian quadrature for multiple related integrals. International Conference on Machine Learning, PMLR 80:5369-5378. (Conference) (Preprint)
    • This paper was accepted for a long talk (top 8% of submitted papers).
  • Chen, W. Y., Mackey, L., Gorham, J. Briol, F-X. & Oates, C. J. (2018). Stein points. International Conference on Machine Learning, PMLR 80:843-852. (Conference) (Preprint) (code)

  • Barp, A., Briol, F-X., Kennedy, A. D. & Girolami, M. (2018). Geometry and dynamics for Markov chain Monte Carlo. Annual Review of Statistics and Its Applications, Vol. 5:451-471. (Journal) (Preprint)

  • Oates, C. J., Niederer, S., Lee, A., Briol, F-X. & Girolami, M. (2017). Probabilistic models for integration error in the assessment of functional cardiac models. Advances in Neural Information Processing Systems (NeurIPS), pages 109-117. (Conference) (Preprint)

  • Briol, F-X., Oates, C. J., Cockayne, J., Chen, W. Y. & Girolami, M. (2017). On the sampling problem for kernel quadrature. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:586-595. (Conference) (Preprint)

  • Briol, F-X., Oates, C. J., Girolami, M. & Osborne, M. A. (2015). Frank-Wolfe Bayesian Quadrature: probabilistic integration with theoretical guarantees. Advances In Neural Information Processing Systems (NIPS), pages 1162-1170. (Preprint) (Conference)
    • This paper was accepted with a spotlight presentation (top 4.5% of submitted papers).
    • This paper was discussed in the blog of Ingmar Schuster.
  • Barp, A., Barp, E. G., Briol, F-X. & Ueltschi, D. (2015). A numerical study of the 3D random interchange and random loop models. Journal of Physics A: Mathematical and Theoretical, 48(34). (Journal) (Preprint)

Discussions and Opinion Pieces

  • Briol, F-X., Diaz De la O, F. A., Hristov, P. O. (2019). Contributed Discussion of “A Bayesian Conjugate Gradient Method”. To appear in Bayesian Analysis. arXiv:1908.02964. (Preprint)

  • Briol, F-X., Oates, C. J., Girolami, M., Osborne, M. A. & Sejdinovic, D. (2019). Rejoinder for “Probabilistic integration: a role in statistical computation?” Statistical Science, Vol 34, Number 1, pp38-42. (Journal) (Preprint)

  • Briol, F-X. & Girolami, M. (2018) Bayesian numerical methods as a case study for statistical data science, Statistical Data Science (Chapter 6): pp. 99-110. (Book)

  • Briol, F-X., Cockayne, J. & Teymur, O. (2016). Contributed discussion on article by Chkrebtii, Campbell, Calderhead, and Girolami. Bayesian Analysis, 11(4), 1285-1293. (Journal) (Preprint)

Dissertations

  • Briol, F-X. (2019). Statistical computation with kernels. PhD thesis, Department of Statistics, University of Warwick. (PDF)

  • Briol, F-X. (2014). Inference for Hawkes Processes. Masters thesis, Department of Statistics, University of Warwick.