Publications

This page contains all my publications; for more details, see my Google Scholar profile. For a non-technical overview of some of my work, see the webpage for the Fundamentals of Statistical Machine Learning project at the Turing Institute. Alternatively, if you would like a brief introduction to some of the fields I work in and have contributed to, you may prefer to have a look at the following project pages:

Preprints

  • Anastasiou, A., Barp, A., Briol, F-X., Ebner, B., Gaunt, R. E., Ghaderinezhad, F., Gorham, J., Gretton, A., Ley, C., Liu, Q., Mackey, L., Oates, C. J., Reinert, G. & Swan, Y. (2021). Stein’s method meets statistics: A review of some recent developments. arXiv:2105.03481. (Preprint)

  • Mastubara, T., Knoblauch, J., Briol, F-X., Oates, C. J. (2021). Robust generalised Bayesian inference for intractable likelihoods. arXiv:2104.07359. (Preprint) (Code)

  • Si, S., Oates, C. J., Duncan, A. B., Carin, L., Briol. F-X. (2020). Scalable control variates for Monte Carlo methods via stochastic optimization. arXiv:2006.07487. (Preprint) (Video)

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

Published Papers

  • Mastubara, T., Oates, C. J., Briol, F-X. (2021). The ridgelet prior: A covariance function approach to prior specification for Bayesian neural networks. arXiv:2010.08488. Accepted for publication in the Journal of Machine Learning Research. (Preprint) (Video) (Code)

  • Bharti, A., Briol, F-X., Pedersen, T. (2021). A general method for calibrating stochastic radio channel models with kernels. IEEE Transactions on Antennas and Propagation. (Journal) (Preprint) (Code)

  • Wynne, G., Briol, F-X., Girolami, M. (2021). Convergence guarantees for Gaussian process means with misspecified likelihoods and smoothness. Journal of Machine Learning Research, 22 (123), 1-40. (Journal) (Preprint)

  • Bharti, A., Adeogun, R., Cai, X., Fan, W., Briol, F-X., Clavier, L., Pedersen, T. (2020). Joint modeling of received power, mean delay and delay spread for wideband radio channels. IEEE Transactions on Antennas and Propagation. (Journal) (Preprint)

  • Zhu, H., Liu, X., Kang, R., Shen, Z., Flaxman, S., Briol, F-X. (2020). Bayesian probabilistic numerical integration with tree-based models. Neural Information Processing Systems, 5837-5849. (Conference) (Preprint) (Code)

  • Barp, A., Briol, F-X., Duncan, A. B., Girolami, M., Mackey, L. (2019). Minimum Stein discrepancy estimators. Neural Information Processing Systems, 12964-12976. (Conference) (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) (Code)

  • 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, 1-22. (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), 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), 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

  • Zhu, H., Liu, X., Caron, A., Manolopoulou, I. Flaxman, S., Briol, F-X. (2020). Contributed Discussion of “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”. Bayesian Analysis, 15(3), 965-1056. (Journal (pp55-58)) (Preprint)

  • Briol, F-X., Diaz De la O, F. A., Hristov, P. O. (2019). Contributed Discussion of “A Bayesian Conjugate Gradient Method”. Bayesian Analysis, 14(3), 980-984. (Journal) (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, 38-42. (Journal) (Preprint)

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