Chris J. Oates

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 60

Top Topics

Maximum Likelihood Estimation

Differential Equations

Gaussian Kernels

Top Venues

J. Mach. Learn. Res.

Publications

Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates
Reinforcement Learning for Adaptive MCMC. CoRR (2024)
Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
Scalable Monte Carlo for Bayesian Learning. CoRR (2024)
Chris J. Oates, Toni Karvonen, Aretha L. Teckentrup, Marina Strocchi, Steven A. Niederer
Probabilistic Richardson Extrapolation. CoRR (2024)
Zheyang Shen, Chris J. Oates
Operator-informed score matching for Markov diffusion models. CoRR (2024)
Zhuo Sun, Chris J. Oates, François-Xavier Briol
Meta-learning Control Variates: Variance Reduction with Limited Data. UAI (2023)
Congye Wang, Ye Chen, Heishiro Kanagawa, Chris J. Oates
Stein Π-Importance Sampling. NeurIPS (2023)
Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Trans. Mach. Learn. Res. 2023 (2023)
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates
Statistical properties of BayesCG under the Krylov prior. Numerische Mathematik 155 (3-4) (2023)
Matthew Fisher, Chris J. Oates
Gradient-Free Kernel Stein Discrepancy. NeurIPS (2023)
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
Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators. PLoS Comput. Biol. 19 (6) (2023)
Zhuo Sun, Chris J. Oates, François-Xavier Briol
Meta-learning Control Variates: Variance Reduction with Limited Data. CoRR (2023)
Toni Karvonen, Chris J. Oates
Maximum likelihood estimation in Gaussian process regression is ill-posed. J. Mach. Learn. Res. 24 (2023)
Chris J. Oates, Wilfrid S. Kendall, Liam Fleming
A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel. Technometrics 64 (3) (2022)
Jon Cockayne, Matthew M. Graham, Chris J. Oates, Timothy John Sullivan, Onur Teymur
Testing Whether a Learning Procedure is Calibrated. J. Mach. Learn. Res. 23 (2022)
Toni Karvonen, Chris J. Oates
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed. CoRR (2022)
Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. CoRR (2022)
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. CoRR (2022)
Matthew Fisher, Tui Nolan, Matthew Graham, Dennis Prangle, Chris J. Oates
Measure Transport with Kernel Stein Discrepancy. AISTATS (2021)
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. J. Mach. Learn. Res. 22 (2021)
Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. AISTATS (2021)
Toni Karvonen, Chris J. Oates, Mark Girolami
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. Math. Comput. 90 (331) (2021)
Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä
Improved Calibration of Numerical Integration Error in Sigma-Point Filters. IEEE Trans. Autom. Control. 66 (3) (2021)
Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates
Bayesian numerical methods for nonlinear partial differential equations. Stat. Comput. 31 (5) (2021)
Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates
Black Box Probabilistic Numerics. CoRR (2021)
Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates
Bayesian Numerical Methods for Nonlinear Partial Differential Equations. CoRR (2021)
Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid
Probabilistic Iterative Methods for Linear Systems. J. Mach. Learn. Res. 22 (2021)
Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates
Black Box Probabilistic Numerics. NeurIPS (2021)
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization. MCQMC (2020)
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates
A Probabilistic Numerical Extension of the Conjugate Gradient Method. CoRR (2020)
Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid
Probabilistic Iterative Methods for Linear Systems. CoRR (2020)
Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. SIAM/ASA J. Uncertain. Quantification 8 (3) (2020)
Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. CoRR (2020)
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. CoRR (2020)
Toni Karvonen, Chris J. Oates, Mark Girolami
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. CoRR (2020)
Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions. CoRR (2020)
Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup
A Locally Adaptive Bayesian Cubature Method. AISTATS (2020)
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. CoRR (2020)
Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami
Bayesian Probabilistic Numerical Methods. SIAM Rev. 61 (4) (2019)
Martin Ehler, Manuel Gräf, Chris J. Oates
Optimal Monte Carlo integration on closed manifolds. Stat. Comput. 29 (6) (2019)
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates
Stein Point Markov Chain Monte Carlo. ICML (2019)
Toni Karvonen, Simo Särkkä, Chris J. Oates
Symmetry exploits for Bayesian cubature methods. Stat. Comput. 29 (6) (2019)
Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, Sach Mukherjee
Causal Learning via Manifold Regularization. J. Mach. Learn. Res. 20 (2019)
Chris J. Oates, Timothy John Sullivan
A modern retrospective on probabilistic numerics. Stat. Comput. 29 (6) (2019)
Mark A. Girolami, Ilse C. F. Ipsen, Chris J. Oates, Art B. Owen, Timothy John Sullivan
Editorial: special edition on probabilistic numerics. Stat. Comput. 29 (6) (2019)
Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates
Stein Points. CoRR (2018)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR (2018)
Jon Cockayne, Chris J. Oates, Mark A. Girolami
A Bayesian Conjugate Gradient Method. CoRR (2018)
Toni Karvonen, Chris J. Oates, Simo Särkkä
A Bayes-Sard Cubature Method. NeurIPS (2018)
Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä
Improved Calibration of Numerical Integration Error in Sigma-Point Filters. CoRR (2018)
Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates
Stein Points. ICML (2018)
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. CoRR (2017)
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
On the Sampling Problem for Kernel Quadrature. ICML (2017)
Chris J. Oates, Steven A. Niederer, Angela W. C. Lee, François-Xavier Briol, Mark A. Girolami
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. NIPS (2017)
François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami
On the Sampling Problem for Kernel Quadrature. CoRR (2017)
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
Bayesian Probabilistic Numerical Methods. CoRR (2017)
Nial Friel, James P. McKeone, Chris J. Oates, Anthony N. Pettitt
Investigation of the widely applicable Bayesian information criterion. Stat. Comput. 27 (3) (2017)
Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens
Exact estimation of multiple directed acyclic graphs. Stat. Comput. 26 (4) (2016)
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. CoRR (2016)
Chris J. Oates, Mark A. Girolami
Control Functionals for Quasi-Monte Carlo Integration. AISTATS (2016)
Chris J. Oates, Jim Q. Smith, Sach Mukherjee
Estimating Causal Structure Using Conditional DAG Models. J. Mach. Learn. Res. 17 (2016)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Probabilistic Integration. CoRR (2015)
Chris J. Oates, Lilia Carneiro da Costa, Tom E. Nichols
Toward a Multisubject Analysis of Neural Connectivity. Neural Comput. 27 (1) (2015)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. NIPS (2015)
Chris J. Oates, Sach Mukherjee
Joint Structure Learning of Multiple Non-Exchangeable Networks. AISTATS (2014)
Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee
Causal network inference using biochemical kinetics. Bioinform. 30 (17) (2014)
Chris J. Oates, Bryan T. J. Hennessy, Yiling Lu, Gordon B. Mills, Sach Mukherjee
Network inference using steady-state data and Goldbeter-Koshland kinetics. Bioinform. 29 (6) (2013)
Chris J. Oates, Bryan T. J. Hennessy, Yiling Lu, Gordon B. Mills, Sach Mukherjee
Network inference using steady-state data and Goldbeter-koshland kinetics. Bioinform. 28 (18) (2012)