François-Xavier Briol
@fxbriol.bsky.social
Professor of Statistics and Machine Learning at UCL Statistical Science. Interested in computational statistics, machine learning and applications in the sciences & engineering.
Just finished delivering a course on 'Robust and scalable simulation-based inference (SBI)' at Greek Stochastics. This covered an introduction to SBI, open challenges, and some recent contributions from my own group.
The slides are now available here: fxbriol.github.io/pdfs/slides-....
The slides are now available here: fxbriol.github.io/pdfs/slides-....
August 28, 2025 at 11:46 AM
Just finished delivering a course on 'Robust and scalable simulation-based inference (SBI)' at Greek Stochastics. This covered an introduction to SBI, open challenges, and some recent contributions from my own group.
The slides are now available here: fxbriol.github.io/pdfs/slides-....
The slides are now available here: fxbriol.github.io/pdfs/slides-....
New ICML 2025 paper: Nested expectations with kernel quadrature.
We propose an algorithm to estimate nested expectations which provides orders of magnitude improvements in low-to-mid dimensional smooth nested expectations using kernel ridge regression/kernel quadrature.
arxiv.org/abs/2502.18284
We propose an algorithm to estimate nested expectations which provides orders of magnitude improvements in low-to-mid dimensional smooth nested expectations using kernel ridge regression/kernel quadrature.
arxiv.org/abs/2502.18284
May 8, 2025 at 4:29 AM
New ICML 2025 paper: Nested expectations with kernel quadrature.
We propose an algorithm to estimate nested expectations which provides orders of magnitude improvements in low-to-mid dimensional smooth nested expectations using kernel ridge regression/kernel quadrature.
arxiv.org/abs/2502.18284
We propose an algorithm to estimate nested expectations which provides orders of magnitude improvements in low-to-mid dimensional smooth nested expectations using kernel ridge regression/kernel quadrature.
arxiv.org/abs/2502.18284
New ICML 2025 paper: Robust Spatio-Temporal GP Regression!
We propose a new GP method with linear-in-time cost that is provably robust to outliers. Unlike competitors, our method is fully conjugate and requires no expensive variational inference!
📄 arxiv.org/abs/2502.02450
We propose a new GP method with linear-in-time cost that is provably robust to outliers. Unlike competitors, our method is fully conjugate and requires no expensive variational inference!
📄 arxiv.org/abs/2502.02450
May 6, 2025 at 8:33 AM
New ICML 2025 paper: Robust Spatio-Temporal GP Regression!
We propose a new GP method with linear-in-time cost that is provably robust to outliers. Unlike competitors, our method is fully conjugate and requires no expensive variational inference!
📄 arxiv.org/abs/2502.02450
We propose a new GP method with linear-in-time cost that is provably robust to outliers. Unlike competitors, our method is fully conjugate and requires no expensive variational inference!
📄 arxiv.org/abs/2502.02450
Our event on 'Computational Statistics and Machine Learning in the sciences' is taking place tomorrow at the London Mathematical Society.
Our stellar line-up includes Harita Dellaporta, Jason McEwen, Ziheng Yang, Ziheng Yang, Marta Betcke, and Sam Livingstone.
Details: www.ucl.ac.uk/data-science/
Our stellar line-up includes Harita Dellaporta, Jason McEwen, Ziheng Yang, Ziheng Yang, Marta Betcke, and Sam Livingstone.
Details: www.ucl.ac.uk/data-science/
December 4, 2024 at 11:56 AM
Our event on 'Computational Statistics and Machine Learning in the sciences' is taking place tomorrow at the London Mathematical Society.
Our stellar line-up includes Harita Dellaporta, Jason McEwen, Ziheng Yang, Ziheng Yang, Marta Betcke, and Sam Livingstone.
Details: www.ucl.ac.uk/data-science/
Our stellar line-up includes Harita Dellaporta, Jason McEwen, Ziheng Yang, Ziheng Yang, Marta Betcke, and Sam Livingstone.
Details: www.ucl.ac.uk/data-science/