Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne (NIPS, 2015)

Probabilistic Integration. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2015)

On the Sampling Problem for Kernel Quadrature. - François-Xavier Briol; Chris J. Oates; Jon Cockayne; Wilson Ye Chen; Mark A. Girolami (ICML, 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 (NIPS, 2017)

Geometry and Dynamics for Markov Chain Monte Carlo. - Alessandro Barp; François-Xavier Briol; Anthony D. Kennedy; Mark A. Girolami (CoRR, 2017)

Stein Points. - Wilson Ye Chen; Lester W. Mackey; Jackson Gorham; François-Xavier Briol; Chris J. Oates (ICML, 2018)

Bayesian Quadrature for Multiple Related Integrals. - Xiaoyue Xi; François-Xavier Briol; Mark A. Girolami (ICML, 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)

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 (ICML, 2019)

Minimum Stein Discrepancy Estimators. - Alessandro Barp; François-Xavier Briol; Andrew B. Duncan; Mark A. Girolami; Lester W. Mackey (NeurIPS, 2019)

Statistical Inference for Generative Models with Maximum Mean Discrepancy. - François-Xavier Briol; Alessandro Barp; Andrew B. Duncan; Mark A. Girolami (CoRR, 2019)

Contributed Discussion of "A Bayesian Conjugate Gradient Method". - François-Xavier Briol; Francisco Alejandro DiazDelaO; Peter O. Hristov (CoRR, 2019)

Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization. - Shijing Si; Chris J. Oates; Andrew B. Duncan; Lawrence Carin; François-Xavier Briol (MCQMC, 2020)

Bayesian Probabilistic Numerical Integration with Tree-Based Models. - Harrison Zhu; Xing Liu; Ruya Kang; Zhichao Shen; Seth R. Flaxman; François-Xavier Briol (NeurIPS, 2020)

Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models. - George Wynne; François-Xavier Briol; Mark A. Girolami (CoRR, 2020)

Composite Goodness-of-fit Tests with Kernels. - Oscar Key; Tamara Fernandez; Arthur Gretton; François-Xavier Briol (CoRR, 2021)

ProbNum: Probabilistic Numerics in Python. - Jonathan Wenger; Nicholas Krämer; Marvin Pförtner; Jonathan Schmidt 001; Nathanael Bosch; Nina Effenberger; Johannes Zenn; Alexandra Gessner; Toni Karvonen; François-Xavier Briol; Maren Mahsereci; Philipp Hennig (CoRR, 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)

Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. - George Wynne; François-Xavier Briol; Mark Girolami (J. Mach. Learn. Res., 2021)

Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. - Charita Dellaporta; Jeremias Knoblauch; Theodoros Damoulas; François-Xavier Briol (AISTATS, 2022)

Towards Healing the Blindness of Score Matching. - Mingtian Zhang; Oscar Key; Peter Hayes; David Barber; Brooks Paige; François-Xavier Briol (CoRR, 2022)

Multilevel Bayesian Quadrature. - Kaiyu Li; Daniel Giles; Toni Karvonen; Serge Guillas; François-Xavier Briol (AISTATS, 2023)

Robust and Scalable Bayesian Online Changepoint Detection. - Matías Altamirano; François-Xavier Briol; Jeremias Knoblauch (ICML, 2023)

Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. - Ayush Bharti; Masha Naslidnyk; Oscar Key; Samuel Kaski; François-Xavier Briol (ICML, 2023)

Vector-Valued Control Variates. - Zhuo Sun; Alessandro Barp; François-Xavier Briol (ICML, 2023)

Baysian numerical integration with neural networks. - Katharina Ott; Michael Tiemann 001; Philipp Hennig; François-Xavier Briol (UAI, 2023)

Meta-learning Control Variates: Variance Reduction with Limited Data. - Zhuo Sun; Chris J. Oates; François-Xavier Briol (UAI, 2023)

Bayesian Numerical Integration with Neural Networks. - Katharina Ott; Michael Tiemann 001; Philipp Hennig; François-Xavier Briol (CoRR, 2023)

Robust and Conjugate Gaussian Process Regression. - Matías Altamirano; François-Xavier Briol; Jeremias Knoblauch (ICML, 2024)

Outlier-robust Kalman Filtering through Generalised Bayes. - Gerardo Duran-Martin; Matías Altamirano; Alexander Y. Shestopaloff; Leandro Sánchez-Betancourt; Jeremias Knoblauch; Matt Jones 002; François-Xavier Briol; Kevin Patrick Murphy (ICML, 2024)

Conditional Bayesian Quadrature. - Zonghao Chen; Masha Naslidnyk; Arthur Gretton; François-Xavier Briol (UAI, 2024)

On the Robustness of Kernel Goodness-of-Fit Tests. - Xing Liu; François-Xavier Briol (CoRR, 2024)

Cost-aware simulation-based inference. - Ayush Bharti; Daolang Huang; Samuel Kaski; François-Xavier Briol (AISTATS, 2025)

Nested Expectations with Kernel Quadrature. - Zonghao Chen; Masha Naslidnyk; François-Xavier Briol (CoRR, 2025)

A Dictionary of Closed-Form Kernel Mean Embeddings. - François-Xavier Briol; Alexandra Gessner; Toni Karvonen; Maren Mahsereci (CoRR, 2025)

Kernel Quantile Embeddings and Associated Probability Metrics. - Masha Naslidnyk; Siu Lun Chau; François-Xavier Briol; Krikamol Muandet (CoRR, 2025)

Stationary MMD Points for Cubature. - Zonghao Chen; Toni Karvonen; Heishiro Kanagawa; François-Xavier Briol; Chris J. Oates (CoRR, 2025)

Multilevel neural simulation-based inference. - Yuga Hikida; Ayush Bharti; Niall Jeffrey; François-Xavier Briol (CoRR, 2025)