François-Xavier Briol

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 39

Top Topics

Gaussian Process

Statistical Inference

Kolmogorov Smirnov

Numerical Integration

Top Venues

J. Mach. Learn. Res.

Publications

Gerardo Duran-Martin, Matías Altamirano, Alexander Y. Shestopaloff, Leandro Sánchez-Betancourt, Jeremias Knoblauch, Matt Jones, François-Xavier Briol, Kevin Murphy
Outlier-robust Kalman Filtering through Generalised Bayes. CoRR (2024)
Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol
Conditional Bayesian Quadrature. CoRR (2024)
Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch
Robust and Conjugate Gaussian Process Regression. CoRR (2023)
Zhuo Sun, Chris J. Oates, François-Xavier Briol
Meta-learning Control Variates: Variance Reduction with Limited Data. UAI (2023)
Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol
Bayesian Numerical Integration with Neural Networks. CoRR (2023)
Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol
Baysian numerical integration with neural networks. UAI (2023)
Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol
Multilevel Bayesian Quadrature. AISTATS (2023)
Zhuo Sun, Alessandro Barp, François-Xavier Briol
Vector-Valued Control Variates. ICML (2023)
Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch
Robust and Scalable Bayesian Online Changepoint Detection. CoRR (2023)
Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. ICML (2023)
Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch
Robust and Scalable Bayesian Online Changepoint Detection. ICML (2023)
Zhuo Sun, Chris J. Oates, François-Xavier Briol
Meta-learning Control Variates: Variance Reduction with Limited Data. CoRR (2023)
Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol
Towards Healing the Blindness of Score Matching. CoRR (2022)
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. AISTATS (2022)
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. CoRR (2022)
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. J. Mach. Learn. Res. 22 (2021)
George Wynne, François-Xavier Briol, Mark Girolami
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. J. Mach. Learn. Res. 22 (2021)
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
ProbNum: Probabilistic Numerics in Python. CoRR (2021)
Oscar Key, Tamara Fernandez, Arthur Gretton, François-Xavier Briol
Composite Goodness-of-fit Tests with Kernels. CoRR (2021)
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization. MCQMC (2020)
Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, François-Xavier Briol
Bayesian Probabilistic Numerical Integration with Tree-Based Models. CoRR (2020)
Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, François-Xavier Briol
Bayesian Probabilistic Numerical Integration with Tree-Based Models. NeurIPS (2020)
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. CoRR (2020)
George Wynne, François-Xavier Briol, Mark A. Girolami
Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models. CoRR (2020)
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. CoRR (2020)
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates
Stein Point Markov Chain Monte Carlo. ICML (2019)
Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
Minimum Stein Discrepancy Estimators. CoRR (2019)
François-Xavier Briol, Francisco Alejandro DiazDelaO, Peter O. Hristov
Contributed Discussion of "A Bayesian Conjugate Gradient Method". CoRR (2019)
Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
Minimum Stein Discrepancy Estimators. NeurIPS (2019)
François-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark A. Girolami
Statistical Inference for Generative Models with Maximum Mean Discrepancy. CoRR (2019)
Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates
Stein Points. CoRR (2018)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR (2018)
Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami
Bayesian Quadrature for Multiple Related Integrals. CoRR (2018)
Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami
Bayesian Quadrature for Multiple Related Integrals. ICML (2018)
Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates
Stein Points. ICML (2018)
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
On the Sampling Problem for Kernel Quadrature. ICML (2017)
Chris J. Oates, Steven A. Niederer, Angela W. C. Lee, François-Xavier Briol, Mark A. Girolami
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. NIPS (2017)
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
On the Sampling Problem for Kernel Quadrature. CoRR (2017)
Alessandro Barp, François-Xavier Briol, Anthony D. Kennedy, Mark A. Girolami
Geometry and Dynamics for Markov Chain Monte Carlo. CoRR (2017)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Probabilistic Integration. CoRR (2015)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. NIPS (2015)