Staff Profile

Chris is a Professor in Statistics at Newcastle University in the UK.

His research spans Statistics, Machine Learning, and Computational Mathematics, and his contributions have been recognised with the award of the Research Prize of the Royal Statistical Society (RSS) in 2017, a Lloyd's Register Foundation Fellowship (2019-2021), Alan Turing Institute Fellowships (2020-2025), the Leverhulme Prize for Mathematics and Statistics in 2023, and the William Guy Medal in Bronze from the RSS in 2024.

• Articles

  • Anastasiou A, Barp A, Briol F-X, Ebner B, Gaunt RE, Ghaderinezhad F, Gorham J, Gretton A, Ley C, Liu Q, Mackey L, Oates CJ, Reinert G, Swan Y. Stein’s Method Meets Computational Statistics: A Review of Some Recent Developments. Statistical Science 2023, 38(1), 120-139.
  • Reid TW, Ipsen ICF, Cockayne J, Oates CJ. Statistical properties of BayesCG under the Krylov prior. Numerische Mathematik 2023, 155, 239-288.
  • Reid TW, Ipsen ICF, Cockayne J, Oates CJ. Statistical properties of BayesCG under the Krylov prior. Numerische Mathematik 2023, 155, 239–288.
  • Hubbert S, Porcu E, Oates CJ, Girolami M. Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Transactions on Machine Learning Research 2023.
  • South LF, Oates CJ, Mira A, Drovandi C. Regularized Zero-Variance Control Variates. Bayesian Analysis 2023, 18(3), 865-888.
  • Karvonen T, Oates CJ. Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed. Journal of Machine Learning Research 2023, 24(120), 1-47.
  • Matsubara T, Knoblauch J, Briol F-X, Oates CJ. Generalized Bayesian Inference for Discrete Intractable Likelihood. Journal of the American Statistical Association 2024, 119(547), 2345-2355.
  • Strocchi M, Longobardi S, Augustin CM, Gsell MAF, Petras A, Rinaldi CA, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators. PLoS Computational Biology 2023, 19(6), e1011257.
  • Cockayne J, Graham MM, Oates CJ, Sullivan TJ. Testing Whether a Learning Procedure is Calibrated. Journal of Machine Learning Research 2022, 23(203), 1-36.
  • South LF, Karvonen T, Nemeth C, Girolami M, Oates CJ. Semi-Exact Control Functionals From Sard's Method. Biometrika 2022, 109(2), 351–367.
  • Matsubara T, Knoblauch J, Briol F-X, Oates CJ. Robust generalised Bayesian inference for intractable likelihoods. Journal of the Royal Statistical Society. Series B: Statistical Methodology 2022, 84(3), 997-1022.
  • Riabiz M, Chen WY, Cockayne J, Swietach P, Niederer SA, Mackey L, Oates CJ. Optimal thinning of MCMC output. Journal of the Royal Statistical Society. Series B: Statistical Methodology 2022, 84(4), 1059-1081.
  • Oates CJ, Kendall WS, Fleming L. A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel. Technometrics 2022, 64(3), 370-383.
  • Barp A, Oates CSJ, Porcu ELIO, Rolami MGI. A Riemann–Stein kernel method. Bernoulli 2022, 28(4), 2181-2208.
  • Matsubara T, Oates CJ, Briol F-X. The ridgelet prior: A covariance function approach to prior specification for Bayesian neural networks. Journal of Machine Learning Research 2021, 22, 1-57.
  • Cockayne J, Ipsen ICF, Oates CJ, Reid TW. Probabilistic Iterative Methods for Linear Systems. Journal of Machine Learning Research 2021, 22(232), 1-34.
  • Karvonen T, Oates CJ, Girolami M. Integration In Reproducing Kernel Hilbert Spaces Of Gaussian Kernels. Mathematics of Computation 2021, 90(331), 2209-2233.
  • Prüher J, Karvonen T, Oates CJ, Straka O, Särkkä S. Improved Calibration of Numerical Integration Error in Sigma-Point Filters. IEEE Transactions on Automatic Control 2021, 66(3), 1286-1292.
  • Stephenson V, Oates CJ, Finlayson A, Thomas C, Wilson KJ. Causal Graphical Models for Systems-Level Engineering Assessment. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2021, 7(2), 04021011.
  • Wang J, Cockayne J, Chkrebtii O, Sullivan TJ, Oates CJ. Bayesian numerical methods for nonlinear partial differential equations. Statistics and Computing 2021, 31(5), 55.
  • Dodwell TJ, Fleming LR, Buchanan C, Kyvelou P, Detommaso G, Gosling PD, Scheichl R, Kendall WS, Gardner L, Girolami MA, Oates CJ. A Data-Centric Approach to Generative Modelling for 3D-Printed Steel. Proceedings of the Royal Society A 2021, 477(2255), 20210444.
  • Karvonen T, Wynne G, Tronarp F, Oates CJ, Särkkä S. Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. SIAM/ASA Journal of Uncertainty Quantification 2020, 8(3), 926-958.
  • Wang J, Cockayne J, Oates CJ. A Role for Symmetry in the Bayesian Solution of Differential Equations. Bayesian Analysis 2020, 15(4), 1057-1085.
  • Karvonen T, Särkkä S, Oates CJ. Symmetry Exploits for Bayesian Cubature Methods. Statistics and Computing 2019, 29(6), 1231-1248.
  • Briol F-X, Oates CJ, Girolami M, Osborne MA, Sejdinovic D. Rejoinder: Probabilistic Integration: A Role in Statistical Computation?. Statistical Science 2019, 34(1), 38-42.
  • Briol FX, Oates CJ, Girolami M, Osborne MA, Sejdinovic D. Probabilistic Integration: A Role in Statistical Computation?. Statistical Science 2019, 34(1), 1-22.
  • Ehler M, Gräf M, Oates CJ. Optimal Monte Carlo integration on closed manifolds. Statistics and Computing 2019, 29(6), 1203-1214.
  • Oates CJ, Cockayne J, Briol F-X, Girolami M. Convergence rates for a class of estimators based on Stein’s method. Bernoulli 2019, 25(2), 1141-1159.
  • Hill SM, Oates CJ, Blythe DA, Mukherjee S. Causal learning via manifold regularization. Journal of Machine Learning Research 2019, 20, 127.
  • Oates CJ, Cockayne J, Aykroyd RG, Girolami M. Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment. Journal of the American Statistical Association 2019, 114(528), 1518-1531.
  • Cockayne J, Oates CJ, Sullivan T, Girolami M. Bayesian Probabilistic Numerical Methods. SIAM Review 2019, 61(4), 756-789.
  • Oates CJ, Sullivan TJ. A modern retrospective on probabilistic numerics. Statistics and Computing 2019, 29, 1335–1351.
  • Cockayne J, Oates CJ, Ipsen I, Girolami M. A Bayesian Conjugate Gradient Method. Bayesian Analysis 2019, 14(3), 937-1012.
  • Oates CJ, Kasza J, Simpson JA, Forbes AB. Repair of Partly Misspecified Causal Diagrams. Epidemiology 2017, 28(4), 548-552.
  • Friel N, McKeone JP, Oates CJ, Pettitt AN. Investigation of the Widely Applicable Bayesian Information Criteria. Statistics and Computing 2017, 27(3), 833-844.
  • Oates CJ, Girolami M, Chopin N. Control functionals for Monte Carlo integration. Journal of the Royal Statistical Society, Series B 2017, 79(3), 695-718.
  • Oates CJ, Papamarkou T, Girolami M. The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation. Journal of the American Statistical Association 2016, 111(514), 634-645.
  • Harjanto D, Papamarkou T, Oates CJ, Rayon-Estrada V, Papavasiliou FN, Papavasiliou A. RNA editing generates sequence diversity within cell populations. Nature Communications 2016, 7, 12145.
  • Friel N, Mira A, Oates CJ. Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods. Bayesian Analysis 2016, 11(1), 215-245.
  • Oates CJ, Smith JQ, Mukherjee S, Cussens J. Exact Estimation of Multiple Directed Acyclic Graphs. Statistics and Computing 2016, 26(4), 797-811.
  • Oates CJ, Smith JQ, Mukherjee S. Estimating Causal Structure Using Conditional DAG Models. Journal of Machine Learning Research 2016, 17(54), 1-23.
  • Oates CJ, Costa L, Nichols T. Towards a Multisubject Analysis of Neural Connectivity. Neural Computation 2015, 27(1), 151–170.
  • Korkola JE, Collisson EA, Heiser L, Oates CJ, Bayani N, Itani S, Esch A, Thompson W, Griffith OL, Wang NJ, Kuo W-L, Cooper B, Billig J, Ziyad S, Hung JL, Jakkula L, Lu Y, Mills G, Spellman PT, Tomlin C, Mukherjee S, Gray JW. Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer. PLoS One 2015, 10(7), e0133219.
  • Oates CJ. Accelerated Non-parametrics for Cascades of Poisson Processes. Stat 2015, 4(1), 183-195.
  • Casale FP, Giurato G, Nassa G, Armond J, Oates CJ, Corà D, Gamba A, Mukherjee S, Weisz A, Nicodemi M. Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS One 2014, 9(2), e88485.
  • Oates CJ, Amos R, Spencer SEF. Quantifying the Multi-Scale Performance of Network Inference Algorithms. Statistical Applications in Genetics and Molecular Biology 2014, 13(5), 611-631.
  • Oates CJ, Korkola J, Gray JW, Mukherjee S. Joint Estimation of Multiple Related Biological Networks. The Annals of Applied Statistics 2014, 8(3), 1892-1919.
  • Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW, Mukherjee S. Causal network inference using biochemical kinetics. Bioinformatics 2014, 30(17), i468-i474.
  • Armond J, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M, Mukherjee S. A stochastic model dissects cell states in biological transition processes. Nature Scientific Reports 2014, 4, 3692.
  • Oates CJ, Hennessy BT, Lu Y, Mills GB, Mukherjee S. Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics. Bioinformatics 2012, 28(18), 2342-2348.
  • Oates CJ, Mukherjee S. Network Inference and Biological Dynamics. The Annals of Applied Statistics 2012, 6(3), 1209-1235.

• Book Chapters

  • Oates CJ, Cockayne J, Prangle D, Sullivan TJ, Girolami M. Optimality Criteria for Probabilistic Numerical Methods. In: Hickernell FJ; Kritzer P, ed. Multivariate Algorithms and Information-Based Complexity. Berlin: De Gruyter, 2020, pp.65-88.
  • Mukherjee S, Oates CJ. Graphical Models in Molecular Systems Biology. In: Maathuis, M; Drton, M; Lauritzen, S; Wainwright, M, ed. Handbook of Graphical Models. CRC Press, 2018, pp.536.
  • Chau Y-X, Oates CJ, Rana AA, Robinson L, Nicodemi M. Self Organisation and Emergence. In: Ball R, Kolokoltsov V, MacKay R, ed. Complexity Science: The Warwick Master’s Course (London Mathematical Society Lecture Note Series). 2013.

• Conference Proceedings (inc. Abstracts)

  • Wang C, Chen WY, Kanagawa H, Oates CJ. Stein Π-Importance Sampling. In: Advances in Neural Information Processing Systems. 2023, New Orleans, LA, USA: Neural Information Processing Systems Foundation.
  • Sun Z, Oates CJ, Briol F-X. Meta-learning Control Variates: Variance Reduction with Limited Data. In: Uncertainty in Artificial Intelligence. 2023, Pittsburgh, PA, USA: ML Research Press.
  • Fisher M, Oates CJ. Gradient-Free Kernel Stein Discrepancy. In: Thirty-seventh Annual Conference on advances in Neural Information Processing Systems NeurIPS 2023. 2023, New Orleans.
  • Fisher MA, Oates CJ. Gradient-Free Kernel Stein Discrepancy. In: Advances in Neural Information Processing Systems 37 (NeurIPS 2023). 2023, New Orleans: Neural Information Processing Systems Foundation.
  • Si S, Oates CJ, Duncan AB, Carin L, Briol F-X. Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. In: 14th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing (MCQMC). 2022, Virtual: Springer.
  • Strocchi M, Longobardi S, Augustin CM, Gsell MAF, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators. In: 10th Vienna International Conference on Mathematical Modelling. 2022, Vienna, Austria: ARGESIM.
  • Oates CJ. Minimum Kernel Discrepancy Estimators. In: Monte Carlo and Quasi-Monte Carlo Methods 2022. 2022, Linz, Austria: Springer Verlag. In Press.
  • Teymur O, Gorham J, Riabiz M, Oates CJ. Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. In: International Conference on Artificial Intelligence and Statistics (AISTATS 2021). 2021, San Diego / Virtual: ML Research Press.
  • Teymur O, Gorham J, Riabiz M, Oates CJ. Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. In: International Conference on Artificial Intelligence and Statistics (AISTATS). 2021, Virtual: ML Research Press.
  • Fisher MA, Nolan T, Graham MM, Prangle D, Oates CJ. Measure Transport with Kernel Stein Discrepancy. In: International Conference on Artificial Intelligence and Statistics (AISTATS 2021). 2021, San Diego / Virtual: ML Research Press.
  • Fisher MA, Nolan TH, Graham MM, Prangle D, Oates CJ. Measure Transport with Kernel Stein Discrepancy. In: 24th International Conference on Artificial Intelligence and Statistics. 2021, Virtual: ML Research Press.
  • Teymur O, Foley CF, Breen PG, Karvonen T, Oates CJ. Black Box Probabilistic Numerics. In: Advances in Neural Information Processing Systems (NeurIPS 2021). 2021, Virtual.
  • Fisher MA, Oates CJ, Powell C, Teckentrup A. A Locally Adaptive Bayesian Cubature Method. In: The 23rd International Conference on Artificial Intelligence and Statistics. 2020, Virtual: ML Research Press.
  • Chen WY, Barp A, Briol FX, Gorham J, Girolami M, Mackey L, Oates CJ. Stein Point Markov Chain Monte Carlo. In: 36th International Conference on Machine Learning (ICML). 2019, Long Beach, CA, USA: Proceedings of Machine Learning Research.
  • Chen WY, Mackey L, Gorham J, Briol F-X, Oates CJ. Stein Points. In: Proceedings of the 35th International Conference on Machine Learning. 2018, Stockholm, Sweden: Proceedings of Machine Learning Research.
  • Wang J, Cockayne J, Oates CJ. On the Bayesian Solution of Differential Equations. In: 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. 2018, London, UK.
  • Karvonen T, Oates CJ, Särkkä S. A Bayes-Sard Cubature Method. In: Thirty-second Conference on Neural Information Processing Systems. 2018, Montreal, Canada.
  • Oates CJ, Niederer S, Lee A, Briol F-X, Girolami M. Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models. In: Advances in Neural Information Processing Systems (NIPS). 2017, Long Beach, CA, USA.
  • Briol FX, Oates CJ, Cockayne J, Chen WY, Girolami M. On the Sampling Problem for Kernel Quadrature. In: 34th International Conference on Machine Learning (ICML). 2017, Sydney, Australia.
  • Cockayne J, Oates CJ, Sullivan T, Girolami M. Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. In: 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt). 2016, Ghent, Belgium: AIP Publishing.
  • Oates CJ, Girolami M. Control Functionals for Quasi-Monte Carlo Integration. In: Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS). 2016, Cadiz, Spain: PMLR.
  • Briol F-X, Oates CJ, Girolami M, Osborne MA. Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. In: 28th International Conference on Neural Information Processing Systems (NIPS 2015). 2015, Montreal, Canada: MIT Press.
  • Oates CJ, Mukherjee S. Joint Structure Learning of Multiple Non-Exchangeable Networks. In: Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS). 2014, Reykjavik, Iceland.

• Editorial

  • Girolami M, Ipsen ICF, Oates CJ, Owen AB, Sullivan TJ. Editorial: special edition on probabilistic numerics. Statistics and Computing 2019, 29, 1181-1183.

• Reviews

  • Oates CJ. Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting. SIAM Review 2023, 65(3), 905-915.
  • South LF, Riabiz M, Teymur O, Oates CJ. Postprocessing of MCMC. Annual Reviews of Statistics and its Application 2022, 9, 529-555.