Chris J. Oates

72 papers A* 7A 8Journal 54Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Probabilistic Inference and Learning with Stein's Method.
CoRR
Qiang Liu, Lester Mackey, Chris J. Oates
2025 J jnl
Harnessing the Power of Reinforcement Learning for Adaptive MCMC.
CoRR
Congye Wang, Matthew A. Fisher, Heishiro Kanagawa, Wilson Ye Chen, Chris J. Oates
2025 A conf
Prediction-Centric Uncertainty Quantification via MMD.
AISTATS
Zheyang Shen, Jeremias Knoblauch, Samuel Power, Chris J. Oates
2025 A conf
Reinforcement Learning for Adaptive MCMC.
AISTATS
Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates
2025 J jnl
Stationary MMD Points for Cubature.
CoRR
Zonghao Chen, Toni Karvonen, Heishiro Kanagawa, François-Xavier Briol, Chris J. Oates
2024 J jnl
Operator-informed score matching for Markov diffusion models.
CoRR
Zheyang Shen, Chris J. Oates
2024 J jnl
Probabilistic Richardson Extrapolation.
CoRR
Chris J. Oates, Toni Karvonen, Aretha L. Teckentrup, Marina Strocchi, Steven A. Niederer
2024 J jnl
Reinforcement Learning for Adaptive MCMC.
CoRR
Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates
2024 J jnl
Scalable Monte Carlo for Bayesian Learning.
CoRR
Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
2023 J jnl
Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.
PLoS Comput. Biol.
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
2023 A* conf
Gradient-Free Kernel Stein Discrepancy.
NeurIPS
Matthew Fisher, Chris J. Oates
2023 J jnl
Maximum likelihood estimation in Gaussian process regression is ill-posed.
J. Mach. Learn. Res.
Toni Karvonen, Chris J. Oates
2023 A conf
Meta-learning Control Variates: Variance Reduction with Limited Data.
UAI
Zhuo Sun, Chris J. Oates, François-Xavier Briol
2023 J jnl
Meta-learning Control Variates: Variance Reduction with Limited Data.
CoRR
Zhuo Sun, Chris J. Oates, François-Xavier Briol
2023 J jnl
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres.
Trans. Mach. Learn. Res.
Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami
2023 J jnl
Statistical properties of BayesCG under the Krylov prior.
Numerische Mathematik
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates
2023 A* conf
Stein Π-Importance Sampling.
NeurIPS
Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates
2022 J jnl
A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel.
Technometrics
Chris J. Oates, Wilfrid S. Kendall, Liam Fleming
2022 J jnl
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed.
CoRR
Toni Karvonen, Chris J. Oates
2022 J jnl
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres.
CoRR
Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami
2022 J jnl
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG.
CoRR
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates
2022 J jnl
Testing Whether a Learning Procedure is Calibrated.
J. Mach. Learn. Res.
Jon Cockayne, Matthew M. Graham, Chris J. Oates, Timothy John Sullivan, Onur Teymur
2021 J jnl
Bayesian Numerical Methods for Nonlinear Partial Differential Equations.
CoRR
Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates
2021 J jnl
Bayesian numerical methods for nonlinear partial differential equations.
Stat. Comput.
Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates
2021 A* conf
Black Box Probabilistic Numerics.
NeurIPS
Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates
2021 J jnl
Black Box Probabilistic Numerics.
CoRR
Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates
2021 J jnl
Improved Calibration of Numerical Integration Error in Sigma-Point Filters.
IEEE Trans. Autom. Control.
Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä
2021 J jnl
Integration in reproducing kernel Hilbert spaces of Gaussian kernels.
Math. Comput.
Toni Karvonen, Chris J. Oates, Mark Girolami
2021 A conf
Measure Transport with Kernel Stein Discrepancy.
AISTATS
Matthew Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates
2021 A conf
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy.
AISTATS
Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates
2021 J jnl
Probabilistic Iterative Methods for Linear Systems.
J. Mach. Learn. Res.
Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid
2021 J jnl
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks.
J. Mach. Learn. Res.
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
2020 A conf
A Locally Adaptive Bayesian Cubature Method.
AISTATS
Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup
2020 J jnl
A Probabilistic Numerical Extension of the Conjugate Gradient Method.
CoRR
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates
2020 J jnl
Integration in reproducing kernel Hilbert spaces of Gaussian kernels.
CoRR
Toni Karvonen, Chris J. Oates, Mark Girolami
2020 J jnl
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions.
SIAM/ASA J. Uncertain. Quantification
Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä
2020 J jnl
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions.
CoRR
Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä
2020 J jnl
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy.
CoRR
Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates
2020 J jnl
Probabilistic Iterative Methods for Linear Systems.
CoRR
Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid
2020 — conf
Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization.
MCQMC
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
2020 J jnl
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization.
CoRR
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
2020 J jnl
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks.
CoRR
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
2019 J jnl
A modern retrospective on probabilistic numerics.
Stat. Comput.
Chris J. Oates, Timothy John Sullivan
2019 J jnl
Bayesian Probabilistic Numerical Methods.
SIAM Rev.
Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami
2019 J jnl
Causal Learning via Manifold Regularization.
J. Mach. Learn. Res.
Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, Sach Mukherjee
2019 J jnl
Editorial: special edition on probabilistic numerics.
Stat. Comput.
Mark A. Girolami, Ilse C. F. Ipsen, Chris J. Oates, Art B. Owen, Timothy John Sullivan
2019 J jnl
Optimal Monte Carlo integration on closed manifolds.
Stat. Comput.
Martin Ehler, Manuel Gräf, Chris J. Oates
2019 A* conf
Stein Point Markov Chain Monte Carlo.
ICML
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates
2019 J jnl
Symmetry exploits for Bayesian cubature methods.
Stat. Comput.
Toni Karvonen, Simo Särkkä, Chris J. Oates
2018 A* conf
A Bayes-Sard Cubature Method.
NeurIPS
Toni Karvonen, Chris J. Oates, Simo Särkkä
2018 J jnl
A Bayesian Conjugate Gradient Method.
CoRR
Jon Cockayne, Chris J. Oates, Mark A. Girolami
2018 J jnl
Improved Calibration of Numerical Integration Error in Sigma-Point Filters.
CoRR
Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä
2018 J jnl
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?".
CoRR
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
2018 A* conf
Stein Points.
ICML
Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates
2018 J jnl
Stein Points.
CoRR
Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates
2017 J jnl
Bayesian Probabilistic Numerical Methods.
CoRR
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
2017 J jnl
Investigation of the widely applicable Bayesian information criterion.
Stat. Comput.
Nial Friel, James P. McKeone, Chris J. Oates, Anthony N. Pettitt
2017 A* conf
On the Sampling Problem for Kernel Quadrature.
ICML
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
2017 J jnl
On the Sampling Problem for Kernel Quadrature.
CoRR
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
2017 — conf
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models.
NIPS
Chris J. Oates, Steven A. Niederer, Angela W. C. Lee, François-Xavier Briol, Mark A. Girolami
2017 J jnl
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems.
CoRR
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
2016 A conf
Control Functionals for Quasi-Monte Carlo Integration.
AISTATS
Chris J. Oates, Mark A. Girolami
2016 J jnl
Estimating Causal Structure Using Conditional DAG Models.
J. Mach. Learn. Res.
Chris J. Oates, Jim Q. Smith, Sach Mukherjee
2016 J jnl
Exact estimation of multiple directed acyclic graphs.
Stat. Comput.
Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens
2016 J jnl
Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems.
CoRR
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
2015 — conf
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.
NIPS
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne
2015 J jnl
Probabilistic Integration.
CoRR
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
2015 J jnl
Toward a Multisubject Analysis of Neural Connectivity.
Neural Comput.
Chris J. Oates, Lilia Carneiro da Costa, Tom E. Nichols
2014 J jnl
Causal network inference using biochemical kinetics.
Bioinform.
Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee
2014 A conf
Joint Structure Learning of Multiple Non-Exchangeable Networks.
AISTATS
Chris J. Oates, Sach Mukherjee
2013 J jnl
Network inference using steady-state data and Goldbeter-Koshland kinetics.
Bioinform.
Chris J. Oates, Bryan T. J. Hennessy, Yiling Lu, Gordon B. Mills, Sach Mukherjee
2012 J jnl
Network inference using steady-state data and Goldbeter-koshland kinetics.
Bioinform.
Chris J. Oates, Bryan T. J. Hennessy, Yiling Lu, Gordon B. Mills, Sach Mukherjee