François-Xavier Briol

According to our database, François-Xavier Briol authored at least 29 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Robust and Conjugate Gaussian Process Regression.

Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch

CoRR, 2023

Bayesian Numerical Integration with Neural Networks.

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

CoRR, 2023

Meta-learning Control Variates: Variance Reduction with Limited Data.

Zhuo Sun, Chris J. Oates, François-Xavier Briol

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Baysian numerical integration with neural networks.

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Vector-Valued Control Variates.

Zhuo Sun, Alessandro Barp, François-Xavier Briol

Proceedings of the International Conference on Machine Learning, 2023

Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference.

Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol

Proceedings of the International Conference on Machine Learning, 2023

Robust and Scalable Bayesian Online Changepoint Detection.

Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch

Proceedings of the International Conference on Machine Learning, 2023

Multilevel Bayesian Quadrature.

Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Towards Healing the Blindness of Score Matching.

Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol

CoRR, 2022

Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap.

Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness.

George Wynne, François-Xavier Briol, Mark Girolami

J. Mach. Learn. Res., 2021

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks.

Takuo Matsubara, Chris J. Oates, François-Xavier Briol

J. Mach. Learn. Res., 2021

ProbNum: Probabilistic Numerics in Python.

Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig

CoRR, 2021

Composite Goodness-of-fit Tests with Kernels.

Oscar Key, Tamara Fernandez, Arthur Gretton, François-Xavier Briol

CoRR, 2021

2020

Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models.

George Wynne, François-Xavier Briol, Mark A. Girolami

CoRR, 2020

Bayesian Probabilistic Numerical Integration with Tree-Based Models.

Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization.

Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol

Proceedings of the Monte Carlo and Quasi-Monte Carlo Methods, 2020

2019

Contributed Discussion of "A Bayesian Conjugate Gradient Method".

François-Xavier Briol, Francisco Alejandro DiazDelaO, Peter O. Hristov

CoRR, 2019

Statistical Inference for Generative Models with Maximum Mean Discrepancy.

François-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark A. Girolami

CoRR, 2019

Minimum Stein Discrepancy Estimators.

Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stein Point Markov Chain Monte Carlo.

Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?".

François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic

CoRR, 2018

Bayesian Quadrature for Multiple Related Integrals.

Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami

Proceedings of the 35th International Conference on Machine Learning, 2018

Stein Points.

Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Geometry and Dynamics for Markov Chain Monte Carlo.

Alessandro Barp, François-Xavier Briol, Anthony D. Kennedy, Mark A. Girolami

CoRR, 2017

Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models.

Chris J. Oates, Steven A. Niederer, Angela W. C. Lee, François-Xavier Briol, Mark A. Girolami

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On the Sampling Problem for Kernel Quadrature.

François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami

Proceedings of the 34th International Conference on Machine Learning, 2017

2015

Probabilistic Integration.

François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic

CoRR, 2015

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.

François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015