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)

Joint Structure Learning of Multiple Non-Exchangeable Networks. - Chris J. Oates; Sach Mukherjee (AISTATS, 2014)

Causal network inference using biochemical kinetics. - Chris J. Oates; Frank Dondelinger; Nora Bayani; James Korkola; Joe W. Gray; Sach Mukherjee (Bioinform., 2014)

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne (NIPS, 2015)

Probabilistic Integration. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2015)

Toward a Multisubject Analysis of Neural Connectivity. - Chris J. Oates; Lilia Carneiro da Costa; Tom E. Nichols (Neural Comput., 2015)

Control Functionals for Quasi-Monte Carlo Integration. - Chris J. Oates; Mark A. Girolami (AISTATS, 2016)

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2016)

Estimating Causal Structure Using Conditional DAG Models. - Chris J. Oates; Jim Q. Smith; Sach Mukherjee (J. Mach. Learn. Res., 2016)

Exact estimation of multiple directed acyclic graphs. - Chris J. Oates; Jim Q. Smith; Sach Mukherjee; James Cussens (Stat. Comput., 2016)

On the Sampling Problem for Kernel Quadrature. - François-Xavier Briol; Chris J. Oates; Jon Cockayne; Wilson Ye Chen; Mark A. Girolami (ICML, 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 (NIPS, 2017)

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2017)

Investigation of the widely applicable Bayesian information criterion. - Nial Friel; James P. McKeone; Chris J. Oates; Anthony N. Pettitt (Stat. Comput., 2017)

Stein Points. - Wilson Ye Chen; Lester W. Mackey; Jackson Gorham; François-Xavier Briol; Chris J. Oates (ICML, 2018)

A Bayes-Sard Cubature Method. - Toni Karvonen; Chris J. Oates; Simo Särkkä (NeurIPS, 2018)

A Bayesian Conjugate Gradient Method. - Jon Cockayne; Chris J. Oates; Mark A. Girolami (CoRR, 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)

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 (ICML, 2019)

Causal Learning via Manifold Regularization. - Steven M. Hill; Chris J. Oates; Duncan A. J. Blythe; Sach Mukherjee (J. Mach. Learn. Res., 2019)

Optimal Monte Carlo integration on closed manifolds. - Martin Ehler; Manuel Gräf; 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)

Symmetry exploits for Bayesian cubature methods. - Toni Karvonen; Simo Särkkä; Chris J. Oates (Stat. Comput., 2019)

A modern retrospective on probabilistic numerics. - Chris J. Oates; Timothy John Sullivan (Stat. Comput., 2019)

Bayesian Probabilistic Numerical Methods. - Jon Cockayne; Chris J. Oates; Timothy John Sullivan; Mark A. Girolami (SIAM Rev., 2019)

A Locally Adaptive Bayesian Cubature Method. - Matthew Fisher; Chris J. Oates; Catherine E. Powell; Aretha L. Teckentrup (AISTATS, 2020)

Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization. - Shijing Si; Chris J. Oates; Andrew B. Duncan; Lawrence Carin; François-Xavier Briol (MCQMC, 2020)

A Probabilistic Numerical Extension of the Conjugate Gradient Method. - Tim W. Reid; Ilse C. F. Ipsen; Jon Cockayne; Chris J. Oates (CoRR, 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)

Measure Transport with Kernel Stein Discrepancy. - Matthew Fisher; Tui Nolan; Matthew M. Graham; Dennis Prangle; Chris J. Oates (AISTATS, 2021)

Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. - Onur Teymur; Jackson Gorham; Marina Riabiz; Chris J. Oates (AISTATS, 2021)

Black Box Probabilistic Numerics. - Onur Teymur; Christopher N. Foley; Philip G. Breen; Toni Karvonen; Chris J. Oates (NeurIPS, 2021)

Probabilistic Iterative Methods for Linear Systems. - Jon Cockayne; Ilse C. F. Ipsen; Chris J. Oates; Tim W. Reid (J. Mach. Learn. Res., 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)

Integration in reproducing kernel Hilbert spaces of Gaussian kernels. - Toni Karvonen; Chris J. Oates; Mark Girolami (Math. Comput., 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)

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)

Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. - Tim W. Reid; Ilse C. F. Ipsen; Jon Cockayne; Chris J. Oates (CoRR, 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)

A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel. - Chris J. Oates; Wilfrid S. Kendall; Liam Fleming (Technometrics, 2022)

Gradient-Free Kernel Stein Discrepancy. - Matthew Fisher; Chris J. Oates (NeurIPS, 2023)

Stein Π-Importance Sampling. - Congye Wang; Wilson Ye Chen; Heishiro Kanagawa; Chris J. Oates (NeurIPS, 2023)

Meta-learning Control Variates: Variance Reduction with Limited Data. - Zhuo Sun; Chris J. Oates; François-Xavier Briol (UAI, 2023)

Maximum likelihood estimation in Gaussian process regression is ill-posed. - Toni Karvonen; Chris J. Oates (J. 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)

Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. - Simon Hubbert; Emilio Porcu; Chris J. Oates; Mark Girolami (Trans. Mach. Learn. Res., 2023)

Statistical properties of BayesCG under the Krylov prior. - Tim W. Reid; Ilse C. F. Ipsen; Jon Cockayne; Chris J. Oates (Numerische Mathematik, 2023)

Probabilistic Richardson Extrapolation. - Chris J. Oates; Toni Karvonen; Aretha L. Teckentrup; Marina Strocchi; Steven A. Niederer (CoRR, 2024)

Operator-informed score matching for Markov diffusion models. - Zheyang Shen; Chris J. Oates (CoRR, 2024)

Scalable Monte Carlo for Bayesian Learning. - Paul Fearnhead; Christopher Nemeth; Chris J. Oates; Chris Sherlock (CoRR, 2024)

Prediction-Centric Uncertainty Quantification via MMD. - Zheyang Shen; Jeremias Knoblauch; Samuel Power; Chris J. Oates (AISTATS, 2025)

Reinforcement Learning for Adaptive MCMC. - Congye Wang; Wilson Ye Chen; Heishiro Kanagawa; Chris J. Oates (AISTATS, 2025)

Stationary MMD Points for Cubature. - Zonghao Chen; Toni Karvonen; Heishiro Kanagawa; François-Xavier Briol; Chris J. Oates (CoRR, 2025)

Harnessing the Power of Reinforcement Learning for Adaptive MCMC. - Congye Wang; Matthew A. Fisher; Heishiro Kanagawa; Wilson Ye Chen; Chris J. Oates (CoRR, 2025)