Jon Cockayne

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 22

Top Topics

Finite Element Method

Differential Equations

Conjugate Gradient Method

Gaussian Processes

Top Venues

J. Mach. Learn. Res.

SIAM/ASA J. Uncertain. Quantification

Publications

Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig
Computation-Aware Kalman Filtering and Smoothing. CoRR (2024)
Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä
A probabilistic Taylor expansion with Gaussian processes. 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)
Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
Theoretical Guarantees for the Statistical Finite Element Method. SIAM/ASA J. Uncertain. Quantification 11 (4) (2023)
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)
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. CoRR (2022)
Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
Theoretical Guarantees for the Statistical Finite Element Method. CoRR (2021)
Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä
A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations. CoRR (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)
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)
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, Andrew B. Duncan
Probabilistic Gradients for Fast Calibration of Differential Equation Models. CoRR (2020)
Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid
Probabilistic Iterative Methods for Linear Systems. CoRR (2020)
Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami
Bayesian Probabilistic Numerical Methods. SIAM Rev. 61 (4) (2019)
Simon Bartels, Jon Cockayne, Ilse C. F. Ipsen, Philipp Hennig
Probabilistic linear solvers: a unifying view. Stat. Comput. 29 (6) (2019)
Jon Cockayne, Chris J. Oates, Mark A. Girolami
A Bayesian Conjugate Gradient Method. CoRR (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)
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)
Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami
Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. CoRR (2016)