Chris J. Oates

J. Mach. Learn. Res., 2023

Meta-learning Control Variates: Variance Reduction with Limited Data.

[BibT_eX]

[DOI]

Zhuo Sun

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Stein Π-Importance Sampling.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Gradient-Free Kernel Stein Discrepancy.

[BibT_eX]

[DOI]

Matthew Fisher

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel.

[BibT_eX]

[DOI]

Wilfrid S. Kendall

Liam Fleming

Technometrics, 2022

Testing Whether a Learning Procedure is Calibrated.

[BibT_eX]

[DOI]

Matthew M. Graham

Onur Teymur

J. Mach. Learn. Res., 2022

Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Improved Calibration of Numerical Integration Error in Sigma-Point Filters.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2021

Bayesian numerical methods for nonlinear partial differential equations.

[BibT_eX]

[DOI]

Junyang Wang

Oksana A. Chkrebtii

Stat. Comput., 2021

Integration in reproducing kernel Hilbert spaces of Gaussian kernels.

[BibT_eX]

[DOI]

Mark Girolami

Math. Comput., 2021

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks.

[BibT_eX]

[DOI]

Takuo Matsubara

J. Mach. Learn. Res., 2021

Probabilistic Iterative Methods for Linear Systems.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Black Box Probabilistic Numerics.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Measure Transport with Kernel Stein Discrepancy.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2020

A Probabilistic Numerical Extension of the Conjugate Gradient Method.

[BibT_eX]

[DOI]

CoRR, 2020

Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization.

[BibT_eX]

[DOI]

Proceedings of the Monte Carlo and Quasi-Monte Carlo Methods, 2020

A Locally Adaptive Bayesian Cubature Method.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Bayesian Probabilistic Numerical Methods.

[BibT_eX]

[DOI]

SIAM Rev., 2019

A modern retrospective on probabilistic numerics.

[BibT_eX]

[DOI]

Stat. Comput., 2019

Symmetry exploits for Bayesian cubature methods.

[BibT_eX]

[DOI]

Simo Särkkä

Stat. Comput., 2019

Editorial: special edition on probabilistic numerics.

[BibT_eX]

[DOI]

Stat. Comput., 2019

Optimal Monte Carlo integration on closed manifolds.

[BibT_eX]

[DOI]

Martin Ehler

Manuel Gräf

Stat. Comput., 2019

Causal Learning via Manifold Regularization.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

Stein Point Markov Chain Monte Carlo.

[BibT_eX]

[DOI]

Wilson Ye Chen

Alessandro Barp

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?".

[BibT_eX]

[DOI]

CoRR, 2018

A Bayesian Conjugate Gradient Method.

[BibT_eX]

[DOI]

CoRR, 2018

A Bayes-Sard Cubature Method.

[BibT_eX]

[DOI]

Simo Särkkä

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Stein Points.

[BibT_eX]

[DOI]

Wilson Ye Chen

Lester W. Mackey

Jackson Gorham

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Investigation of the widely applicable Bayesian information criterion.

[BibT_eX]

[DOI]

Stat. Comput., 2017

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems.

[BibT_eX]

[DOI]

CoRR, 2017

Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models.

[BibT_eX]

[DOI]

Steven A. Niederer

Angela W. C. Lee

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On the Sampling Problem for Kernel Quadrature.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Exact estimation of multiple directed acyclic graphs.

[BibT_eX]

[DOI]

Stat. Comput., 2016

Estimating Causal Structure Using Conditional DAG Models.

[BibT_eX]

[DOI]

Jim Q. Smith

Sach Mukherjee

J. Mach. Learn. Res., 2016

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems.

[BibT_eX]

[DOI]

CoRR, 2016

Control Functionals for Quasi-Monte Carlo Integration.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Toward a Multisubject Analysis of Neural Connectivity.

[BibT_eX]

[DOI]

Lilia Carneiro da Costa

Tom E. Nichols

Neural Comput., 2015

Probabilistic Integration.

[BibT_eX]

[DOI]

CoRR, 2015

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.

[BibT_eX]

[DOI]

Michael A. Osborne

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Causal network inference using biochemical kinetics.

[BibT_eX]

[DOI]

Bioinform., 2014

Joint Structure Learning of Multiple Non-Exchangeable Networks.

[BibT_eX]

[DOI]