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François-Xavier Briol
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2020 – today
- 2024
- [i23]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 abs/2405.05646 (2024) - [i22]Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol:
Conditional Bayesian Quadrature. CoRR abs/2406.16530 (2024) - 2023
- [c16]Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol:
Multilevel Bayesian Quadrature. AISTATS 2023: 1845-1868 - [c15]Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch:
Robust and Scalable Bayesian Online Changepoint Detection. ICML 2023: 642-663 - [c14]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: 2289-2312 - [c13]Zhuo Sun, Alessandro Barp, François-Xavier Briol:
Vector-Valued Control Variates. ICML 2023: 32819-32846 - [c12]Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol:
Baysian numerical integration with neural networks. UAI 2023: 1606-1617 - [c11]Zhuo Sun, Chris J. Oates, François-Xavier Briol:
Meta-learning Control Variates: Variance Reduction with Limited Data. UAI 2023: 2047-2057 - [i21]Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch:
Robust and Scalable Bayesian Online Changepoint Detection. CoRR abs/2302.04759 (2023) - [i20]Zhuo Sun, Chris J. Oates, François-Xavier Briol:
Meta-learning Control Variates: Variance Reduction with Limited Data. CoRR abs/2303.04756 (2023) - [i19]Katharina Ott, Michael Tiemann, Philipp Hennig
, François-Xavier Briol:
Bayesian Numerical Integration with Neural Networks. CoRR abs/2305.13248 (2023) - [i18]Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch:
Robust and Conjugate Gaussian Process Regression. CoRR abs/2311.00463 (2023) - 2022
- [c10]Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol:
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. AISTATS 2022: 943-970 - [i17]Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol:
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. CoRR abs/2202.04744 (2022) - [i16]Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol:
Towards Healing the Blindness of Score Matching. CoRR abs/2209.07396 (2022) - 2021
- [j2]George Wynne, François-Xavier Briol, Mark Girolami:
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness. J. Mach. Learn. Res. 22: 123:1-123:40 (2021) - [j1]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: 157:1-157:57 (2021) - [i15]Oscar Key, Tamara Fernandez, Arthur Gretton, François-Xavier Briol:
Composite Goodness-of-fit Tests with Kernels. CoRR abs/2111.10275 (2021) - [i14]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 abs/2112.02100 (2021) - 2020
- [c9]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: 205-221 - [c8]Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol:
Bayesian Probabilistic Numerical Integration with Tree-Based Models. NeurIPS 2020 - [i13]George Wynne, François-Xavier Briol
, Mark A. Girolami:
Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models. CoRR abs/2001.10818 (2020) - [i12]Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth R. Flaxman, François-Xavier Briol:
Bayesian Probabilistic Numerical Integration with Tree-Based Models. CoRR abs/2006.05371 (2020) - [i11]Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol:
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. CoRR abs/2006.07487 (2020) - [i10]Takuo Matsubara, Chris J. Oates, François-Xavier Briol:
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. CoRR abs/2010.08488 (2020)
2010 – 2019
- 2019
- [c7]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: 1011-1021 - [c6]Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. NeurIPS 2019: 12964-12976 - [i9]François-Xavier Briol, Alessandro Barp
, Andrew B. Duncan, Mark A. Girolami:
Statistical Inference for Generative Models with Maximum Mean Discrepancy. CoRR abs/1906.05944 (2019) - [i8]Alessandro Barp
, François-Xavier Briol
, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. CoRR abs/1906.08283 (2019) - [i7]François-Xavier Briol, Francisco Alejandro DiazDelaO, Peter O. Hristov:
Contributed Discussion of "A Bayesian Conjugate Gradient Method". CoRR abs/1908.02964 (2019) - 2018
- [c5]Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates:
Stein Points. ICML 2018: 843-852 - [c4]Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami:
Bayesian Quadrature for Multiple Related Integrals. ICML 2018: 5369-5378 - [i6]Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami:
Bayesian Quadrature for Multiple Related Integrals. CoRR abs/1801.04153 (2018) - [i5]Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates:
Stein Points. CoRR abs/1803.10161 (2018) - [i4]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR abs/1811.10275 (2018) - 2017
- [c3]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. ICML 2017: 586-595 - [c2]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: 110-118 - [i3]Alessandro Barp
, François-Xavier Briol, Anthony D. Kennedy, Mark A. Girolami:
Geometry and Dynamics for Markov Chain Monte Carlo. CoRR abs/1705.02891 (2017) - [i2]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. CoRR abs/1706.03369 (2017) - 2015
- [c1]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne:
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. NIPS 2015: 1162-1170 - [i1]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Probabilistic Integration. CoRR abs/1512.00933 (2015)
Coauthor Index
aka: Mark Girolami
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last updated on 2024-07-25 20:22 CEST by the dblp team
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