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Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
- François-Xavier Briol, C. Oates, M. Girolami, Michael A. Osborne
- Computer ScienceNIPS
- 8 June 2015
TLDR
Probabilistic Integration: A Role for Statisticians in Numerical Analysis?
- François-Xavier Briol, C. Oates, M. Girolami, Michael A. Osborne, D. Sejdinovic
- Computer Science
- 3 December 2015
TLDR
Minimum Stein Discrepancy Estimators
- A. Barp, François-Xavier Briol, A. Duncan, M. Girolami, Lester W. Mackey
- MathematicsNeurIPS
- 19 June 2019
TLDR
Statistical Inference for Generative Models with Maximum Mean Discrepancy
- François-Xavier Briol, A. Barp, A. Duncan, M. Girolami
- Computer Science, MathematicsArXiv
- 13 June 2019
TLDR
Stein Points
- W. Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, C. Oates
- Computer ScienceICML
- 27 March 2018
TLDR
Bayesian Quadrature for Multiple Related Integrals
- Xiaoyue Xi, François-Xavier Briol, M. Girolami
- Computer ScienceICML
- 12 January 2018
TLDR
Robust generalised Bayesian inference for intractable likelihoods
- Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, C. Oates
- MathematicsJournal of the Royal Statistical Society: Series…
- 15 April 2021
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis‐specification of the…
On the Sampling Problem for Kernel Quadrature
- François-Xavier Briol, C. Oates, J. Cockayne, W. Chen, M. Girolami
- MathematicsICML
- 11 June 2017
TLDR
Stein’s Method Meets Statistics: A Review of Some Recent Developments
- Andreas Anastasiou, A. Barp, Yvik Swan
- Mathematics
- 2021
Stein’s method is a collection of tools for analysing distributional comparisons through the study of a class of linear operators called Stein operators. Originally studied in probability, Stein’s…
Geometry and Dynamics for Markov Chain Monte Carlo
- A. Barp, François-Xavier Briol, A. Kennedy, M. Girolami
- MathematicsArXiv
- 8 May 2017
TLDR
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