Chris J. Oates

According to our database, Chris J. Oates authored at least 51 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Scalable Monte Carlo for Bayesian Learning.

Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock

CoRR, 2024

Operator-informed score matching for Markov diffusion models.

Zheyang Shen, Chris J. Oates

CoRR, 2024

Reinforcement Learning for Adaptive MCMC.

Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates

CoRR, 2024

Probabilistic Richardson Extrapolation.

Chris J. Oates, Toni Karvonen, Aretha L. Teckentrup, Marina Strocchi, Steven A. Niederer

CoRR, 2024

2023

Statistical properties of BayesCG under the Krylov prior.

Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates

Numerische Mathematik, December, 2023

Sobolev Spaces, Kernels and Discrepancies over Hyperspheres.

Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami

Trans. Mach. Learn. Res., 2023

Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.

Marina Strocchi, Stefano Longobardi, Christoph M. Augustin, Matthias A. F. Gsell, Argyrios Petras, Christopher A. Rinaldi, Edward J. Vigmond, Gernot Plank, Chris J. Oates, Richard D. Wilkinson, Steven A. Niederer

PLoS Comput. Biol., 2023

Maximum likelihood estimation in Gaussian process regression is ill-posed.

Toni Karvonen, Chris J. Oates

J. Mach. Learn. Res., 2023

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

Zhuo Sun, Chris J. Oates, François-Xavier Briol

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Stein Π-Importance Sampling.

Congye Wang, Ye Chen, Heishiro Kanagawa, Chris J. Oates

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

Gradient-Free Kernel Stein Discrepancy.

Matthew Fisher, Chris J. Oates

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.

Chris J. Oates, Wilfrid S. Kendall, Liam Fleming

Technometrics, 2022

Testing Whether a Learning Procedure is Calibrated.

Jon Cockayne, Matthew M. Graham, Chris J. Oates, Timothy John Sullivan, Onur Teymur

J. Mach. Learn. Res., 2022

Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG.

Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates

CoRR, 2022

2021

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

Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä

IEEE Trans. Autom. Control., 2021

Bayesian numerical methods for nonlinear partial differential equations.

Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates

Stat. Comput., 2021

Integration in reproducing kernel Hilbert spaces of Gaussian kernels.

Toni Karvonen, Chris J. Oates, Mark Girolami

Math. Comput., 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

Probabilistic Iterative Methods for Linear Systems.

Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid

J. Mach. Learn. Res., 2021

Black Box Probabilistic Numerics.

Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates

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.

Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates

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

Measure Transport with Kernel Stein Discrepancy.

Matthew Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates

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.

Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä

SIAM/ASA J. Uncertain. Quantification, 2020

A Probabilistic Numerical Extension of the Conjugate Gradient Method.

Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates

CoRR, 2020

Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization.

Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol

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

A Locally Adaptive Bayesian Cubature Method.

Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup

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

2019

Bayesian Probabilistic Numerical Methods.

Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami

SIAM Rev., 2019

A modern retrospective on probabilistic numerics.

Chris J. Oates, Timothy John Sullivan

Stat. Comput., 2019

Symmetry exploits for Bayesian cubature methods.

Toni Karvonen, Simo Särkkä, Chris J. Oates

Stat. Comput., 2019

Editorial: special edition on probabilistic numerics.

Mark A. Girolami, Ilse C. F. Ipsen, Chris J. Oates, Art B. Owen, Timothy John Sullivan

Stat. Comput., 2019

Optimal Monte Carlo integration on closed manifolds.

Martin Ehler, Manuel Gräf, Chris J. Oates

Stat. Comput., 2019

Causal Learning via Manifold Regularization.

Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, Sach Mukherjee

J. Mach. Learn. Res., 2019

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

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

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

A Bayesian Conjugate Gradient Method.

Jon Cockayne, Chris J. Oates, Mark A. Girolami

CoRR, 2018

A Bayes-Sard Cubature Method.

Toni Karvonen, Chris J. Oates, Simo Särkkä

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

Stein Points.

Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates

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

2017

Investigation of the widely applicable Bayesian information criterion.

Nial Friel, James P. McKeone, Chris J. Oates, Anthony N. Pettitt

Stat. Comput., 2017

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems.

Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami

CoRR, 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

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.

François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami

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

2016

Exact estimation of multiple directed acyclic graphs.

Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens

Stat. Comput., 2016

Estimating Causal Structure Using Conditional DAG Models.

Chris J. Oates, Jim Q. Smith, Sach Mukherjee

J. Mach. Learn. Res., 2016

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

Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami

CoRR, 2016

Control Functionals for Quasi-Monte Carlo Integration.

Chris J. Oates, Mark A. Girolami

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

2015

Toward a Multisubject Analysis of Neural Connectivity.

Chris J. Oates, Lilia Carneiro da Costa, Tom E. Nichols

Neural Comput., 2015

Probabilistic Integration.

François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic

CoRR, 2015

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

François-Xavier Briol, Chris J. Oates, Mark A. Girolami, 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.

Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee

Bioinform., 2014

Joint Structure Learning of Multiple Non-Exchangeable Networks.

Chris J. Oates, Sach Mukherjee

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Network inference using steady-state data and Goldbeter-Koshland kinetics.

Chris J. Oates, Bryan T. J. Hennessy, Yiling Lu, Gordon B. Mills, Sach Mukherjee

Bioinform., 2013