Browse by UCL people
Group by: Type | Date
Number of items: 45.
2025
Bharti, Ayush;
Huang, Daolang;
Kaski, Samuel;
Briol, François-Xavier;
(2025)
Cost-aware simulation-based inference.
In: Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz, (eds.)
Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS).
(pp. pp. 28-36).
Proceedings of Machine Learning Research (PMLR): Mai Khao, Thailand.
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Naslidnyk, Masha;
Chau, Siu Lun;
Briol, François-Xavier;
Muandet, Krikamol;
(2025)
Kernel Quantile Embeddings and Associated Probability Metrics.
In:
Proceedings of the 42 nd International Conference on Machine Learning.
PMLR: Vancouver, Canada.
(In press).
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2024
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Altamirano, Matias;
Briol, François-Xavier;
Knoblauch, Jeremias;
(2024)
Robust and Conjugate Gaussian Process Regression.
In:
Proceedings of the 41 st International Conference on Machine Learning.
ICML: Vienna, Austria.
(In press).
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Chen, Zonghao;
Naslidnyk, Masha;
Gretton, Arthur;
Briol, François-Xavier;
(2024)
Conditional Bayesian Quadrature.
In:
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence.
Association for Uncertainty in Artificial Intelligence (AUAI)
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Duran-Martin, Gerardo;
Altamirano, Matias;
Shestopaloff, Alexander Y;
Sánchez-Betancourt, Leandro;
Knoblauch, Jeremias;
Jones, Matt;
Briol, François-Xavier;
(2024)
Outlier-robust Kalman Filtering through Generalised Bayes.
In:
Proceedings of the 41st International Conference on Machine Learning (ICML 2024).
ICML: Vienna, Austria.
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Matsubara, T;
Knoblauch, J;
Briol, FX;
Oates, CJ;
(2024)
Generalized Bayesian Inference for Discrete Intractable Likelihood.
Journal of the American Statistical Association
, 119
(547)
pp. 2345-2355.
10.1080/01621459.2023.2257891.
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2023
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Altamirano, M;
Briol, FX;
Knoblauch, J;
(2023)
Robust and Scalable Bayesian Online Changepoint Detection.
In:
Proceedings of the 40th International Conference on Machine Learning.
(pp. pp. 642-663).
PMLR: Honolulu, Hawaii, USA.
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Anastasiou, Andreas;
Barp, Alessandro;
Briol, François-Xavier;
Ebner, Bruno;
Gaunt, Robert E;
Ghaderinezhad, Fatemeh;
Gorham, Jackson;
... Swan, Yvik; + view all
(2023)
Stein's method meets computational statistics: A review of some recent developments.
Statistical Science
, 38
(1)
pp. 120-139.
10.1214/22-STS863.
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Bharti, A;
Naslidnyk, M;
Key, O;
Kaski, S;
Briol, FX;
(2023)
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference.
In: Krause, A and Brunskill, E and Cho, K and Engelhardt, B and Sabato, S and Scarlett, J, (eds.)
Proceedings of the 40th International Conference on Machine Learning.
(pp. pp. 2289-2312).
Proceedings of Machine Learning Research (PMLR): Honolulu, HI, USA.
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Kirby, Andrew;
Briol, François‐Xavier;
Dunstan, Thomas D;
Nishino, Takafumi;
(2023)
Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms.
Wind Energy
10.1002/we.2851.
(In press).
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Li, K;
Giles, D;
Karvonen, T;
Guillas, S;
Briol, FX;
(2023)
Multilevel Bayesian Quadrature.
In:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 1845-1868).
Proceedings of Machine Learning Research (PMLR): Valencia, Spain.
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Ott, K;
Tiemann, M;
Hennig, P;
Briol, FX;
(2023)
Bayesian Numerical Integration with Neural Networks.
In:
Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023).
(pp. pp. 1606-1617).
Proceedings of Machine Learning Research (PMLR)
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Sun, Z;
Barp, A;
Briol, FX;
(2023)
Vector-Valued Control Variates.
In:
Proceedings of Machine Learning Research.
(pp. pp. 32819-32846).
ML Research Press: Honolulu, Hawaii.
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Sun, Zhua;
Oates, Chris J;
Briol, François-Xavier;
(2023)
Meta-learning Control Variates: Variance Reduction with Limited Data.
In: Lawrence, Neil, (ed.)
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 2047-2057).
PMLR: Pittsburgh, PA, USA.
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2022
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Dellaporta, Charita;
Knoblauch, Jeremias;
Damoulas, Theodoros;
Briol, François-Xavier;
(2022)
Robust Bayesian Inference for Simulator-based Models via the MMD
Posterior Bootstrap.
In:
AISTATS 2022 Accepted Papers.
AISTATS
(In press).
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Li, Kaiyu;
Giles, Daniel;
Karvonen, Toni;
Guillas, Serge;
Briol, François-Xavier;
(2022)
Multilevel Bayesian Quadrature.
arXiv: Ithaca, NY, USA.
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Matsubara, Takuo;
Knoblauch, Jeremias;
Briol, François-Xavier;
Oates, Chris J;
(2022)
Generalised Bayesian Inference for Discrete Intractable Likelihood.
arXiv: Ithaca (NY), USA.
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Matsubara, Takuo;
Knoblauch, Jeremias;
Briol, François‐Xavier;
Oates, Chris J;
(2022)
Robust generalised Bayesian inference for intractable likelihoods.
Journal of the Royal Statistical Society: Series B
10.1111/rssb.12500.
(In press).
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Zhang, Mingtian;
Key, Oscar;
Hayes, Peter;
Barber, David;
Paige, Brooks;
Briol, François-Xavier;
(2022)
Towards Healing the Blindness of Score Matching.
arXiv: Ithaca (NY), USA.
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2021
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Bharti, A;
Adeogun, R;
Cai, X;
Fan, W;
Briol, FX;
Clavier, L;
Pedersen, T;
(2021)
Joint Modeling of Received Power, Mean Delay, and Delay Spread for Wideband Radio Channels.
IEEE Transactions on Antennas and Propagation
10.1109/TAP.2021.3060099.
(In press).
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Bharti, A;
Briol, FX;
Pedersen, T;
(2021)
A General Method for Calibrating Stochastic Radio Channel Models with Kernels.
IEEE Transactions on Antennas and Propagation
10.1109/TAP.2021.3083761.
(In press).
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Key, O;
Fernandez, T;
Gretton, A;
Briol, F-X;
(2021)
Composite Goodness-of-fit Tests with Kernels.
In:
NeurIPS 2021 Workshop Your Model Is Wrong: Robustness and Misspecification in Probabilistic Modeling.
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Matsubara, T;
Oates, CJ;
Briol, FX;
(2021)
The ridgelet prior: A covariance function approach to prior specification for bayesian neural networks.
Journal of Machine Learning Research
, 22
pp. 1-57.
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Niu, Ziang;
Meier, Johanna;
Briol, François-Xavier;
(2021)
Discrepancy-based Inference for Intractable Generative Models using
Quasi-Monte Carlo.
arXiv: Ithaca, NY, USA.
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Wenger, Jonathan;
Krämer, Nicholas;
Pförtner, Marvin;
Schmidt, Jonathan;
Bosch, Nathanael;
Effenberger, Nina;
Zenn, Johannes;
... Hennig, Philipp; + view all
(2021)
ProbNum: Probabilistic Numerics in Python.
arXiv: Ithaca (NY), USA.
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Wynne, G;
Briol, FX;
Girolami, M;
(2021)
Convergence guarantees for gaussian process means with misspecified likelihoods and smoothness.
Journal of Machine Learning Research
, 22
, Article 123.
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2020
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Si, S;
Oates, CJ;
Duncan, AB;
Carin, L;
Briol, F-X;
(2020)
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization.
ArXiv: Ithaca, NY, USA.
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Zhu, Harrison;
Liu, Xing;
Caron, Alberto;
Manolopoulou, Ioanna;
Flaxman, Seth;
Briol, Francois-Xavier;
(2020)
Contributed Discussion of “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”.
Bayesian Analysis
, 15
(3)
pp. 55-58.
10.1214/19-BA1195.
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Zhu, H;
Liu, X;
Kang, R;
Shen, Z;
Flaxman, S;
Briol, F-X;
(2020)
Bayesian Probabilistic Numerical Integration with Tree-Based Models.
In: Larochelle, H and Ranzato, M and Hadsell, R and Balcan, M-F and Lin, H-T, (eds.)
34th Conference on Neural Information Processing Systems (NeurIPS 2020).
NeurIPS: Vancouver, Canada.
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2019
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Barp, A;
Briol, F-X;
Duncan, AB;
Girolami, MA;
Mackey, LW;
(2019)
Minimum Stein Discrepancy Estimators.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, E and Garnett., R, (eds.)
Proceedings of Advances in Neural Information Processing Systems 32 (NIPS 2019).
NIPS: Massachusetts, USA.
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Briol, F-X;
Barp, A;
Duncan, AB;
Girolami, M;
(2019)
Statistical Inference for Generative Models with Maximum Mean Discrepancy.
ArXiv: Ithaca, NY, USA.
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Briol, F-X;
DiazDelaO, FA;
Hristov, PO;
(2019)
Contributed Discussion [A Bayesian Conjugate Gradient Method].
Bayesian Analysis
, 14
(3)
pp. 980-984.
10.1214/19-BA1145.
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Briol, FX;
Oates, CJ;
Girolami, M;
Osborne, MA;
Sejdinovic, D;
(2019)
Probabilistic integration: A role in statistical computation?
Statistical Science
, 34
(1)
pp. 1-22.
10.1214/18-STS660.
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Briol, FX;
Oates, CJ;
Girolami, M;
Osborne, MA;
Sejdinovic, D;
(2019)
Rejoinder: Probabilistic integration: A role in statistical computation?
Statistical Science
, 34
(1)
pp. 38-42.
10.1214/18-STS683.
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Chen, WY;
Barp, A;
Briol, F-X;
Gorham, J;
Girolami, M;
Mackey, L;
Oates, CJ;
(2019)
Stein Point Markov Chain Monte Carlo.
In: Chaudhuri, Kamalika and Salakhutdinov, Ruslan, (eds.)
Proceedings of the 36th International Conference on Machine Learning.
(pp. pp. 1011-1021).
Proceedings of Machine Learning Research: Long Beach, California, USA.
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Oates, CJ;
Cockayne, J;
Briol, FX;
Girolami, M;
(2019)
Convergence rates for a class of estimators based on Stein’s method.
Bernoulli
, 25
(2)
pp. 1141-1159.
10.3150/17-BEJ1016.
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2018
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Barp, A;
Briol, FX;
Kennedy, AD;
Girolami, M;
(2018)
Geometry and Dynamics for Markov Chain Monte Carlo.
Annual Review of Statistics and Its Application
, 5
pp. 451-471.
10.1146/annurev-statistics-031017-100141.
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Briol, François-Xavier;
(2018)
Statistical computation with kernels.
Doctoral thesis (Ph.D), University of Warwick.
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Chen, WY;
Mackey, L;
Gorham, J;
Briol, FX;
Oates, CJ;
(2018)
Stein points.
In: Dy, J and Krause, A, (eds.)
Proceedings of the 35th International Conference on Machine Learning.
(pp. pp. 844-853).
PMLR
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Xi, X;
Briol, FX;
Girolami, M;
(2018)
Bayesian quadrature for multiple related integrals.
In: Dy, J and Krause, A, (eds.)
Proceedings of the 35th International Conference on Machine Learning.
(pp. pp. 8533-8564).
PPMLR
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2017
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Briol, FX;
Oates, CJ;
Cockayne, J;
Chen, WY;
Girolami, M;
(2017)
On the sampling problem for Kernel quadrature.
In:
Proceedings of the 34th International Conference on Machine Learning.
(pp. pp. 586-595).
PMLR: Sydney, NSW, Australia.
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Oates, CJ;
Niederer, S;
Lee, A;
Briol, FX;
Girolami, M;
(2017)
Probabilistic models for integration error in the assessment of functional cardiac models.
In:
Advances in Neural Information Processing Systems 30 (NIPS 2017) Proceedings.
(pp. pp. 110-118).
Neural Information Processing Systems Foundation, Inc.: Long Beach, CA, USA.
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2016
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Briol, FX;
Cockayne, J;
Teymur, O;
(2016)
Contributed discussion on article by Chkrebtii, Campbell, Calderhead, and Girolami.
Bayesian Analysis
, 11
(4)
pp. 1285-1293.
10.1214/16-BA1017A.
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2015
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Barp, A;
Barp, EG;
Briol, FX;
Ueltschi, D;
(2015)
A numerical study of the 3D random interchange and random loop models.
Journal of Physics A: Mathematical and Theoretical
, 48
(34)
, Article 345002. 10.1088/1751-8113/48/34/345002.
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Briol, FX;
Oates, CJ;
Girolami, M;
Osborne, MA;
(2015)
Frank-Wolfe Bayesian quadrature: Probabilistic integration with theoretical guarantees.
In:
Advances in Neural Information Processing Systems 28 (NIPS 2015).
Neural Information Processing Systems (NIPS)
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