Work in Progress:

  • Shen Z, Knoblauch J, Power S, Oates CJ. Prediction-Centric Uncertainty Quantification via MMD. [arXiv]

  • Fearnhead P, Nemeth C, Oates CJ, Sherlock C. Scalable Monte Carlo for Bayesian Learning. [arXiv]

  • Shen Z, Oates CJ. Operator-Informed Score Matching for Markov Diffusion Models. [arXiv]

  • Wang C, Chen W, Kanagawa H, Oates CJ. Reinforcement Learning for Adaptive MCMC. [arXiv]

  • Reid TW, Ipsen ICF, Cockayne J, Oates CJ. A Probabilistic Numerical Extension of the Conjugate Gradient Method. [arXiv] [Video]

2024

Featured
Minimum Kernel Discrepancy Estimators
Minimum Kernel Discrepancy Estimators

Oates CJ. Minimum Kernel Discrepancy Estimators. To appear in: A. Hinrichs, P. Kritzer, F. Pillichshammer (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag. [Book] [arXiv]

The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning
The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning

Porcu E, Bevilacqua M, Schaback R, Oates CJ. The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning. Statistical Science, 39(3):469-492. [Journal] [arXiv]

Online Semiparametric Regression via Sequential Monte Carlo
Online Semiparametric Regression via Sequential Monte Carlo

Menictas M, Oates CJ, Wand MP. Online Semiparametric Regression via Sequential Monte Carlo. Australian & New Zealand Journal of Statistics, to appear. [arXiv]

Probabilistic Richardson Extrapolation
Probabilistic Richardson Extrapolation

Oates CJ, Karvonen T, Teckentrup AL, Strocchi M, Niederer SA. Probabilistic Richardson Extrapolation. Journal of the Royal Statistical Society, Series B. To appear. [arXiv]

Grand Challenges in Bayesian Computation
Grand Challenges in Bayesian Computation

Bhattacharya A, Linero A, Oates CJ (2024) Grand Challenges in Bayesian Computation. ISBA Bulletin 31(3). [arXiv]

2023

Featured
Stein Π-Importance Sampling
Stein Π-Importance Sampling

Wang C, Chen WY, Kanagawa H, Oates CJ. Stein Π-Importance Sampling. Advances in Neural Information Processing Systems (NeurIPS 2023) [arXiv]

Selected for spotlight presentation.

Gradient-Free Kernel Stein Discrepancy
Gradient-Free Kernel Stein Discrepancy

Fisher M, Oates CJ. Gradient-Free Kernel Stein Discrepancy. Advances in Neural Information Processing Systems (NeurIPS 2023) [arXiv]

Stein's Method Meets Statistics: A Review of Some Recent Developments
Stein's Method Meets Statistics: A Review of Some Recent Developments

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 Statistics: A Review of Some Recent Developments. Statistical Science, 38(1): 120-139.

[Journal] [arXiv]

Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction
Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction

South LF, Oates CJ, Mira M, Drovandi C. Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction. Bayesian Analysis, 18(3): 865-888.

[Journal] [arXiv] [video]

Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed

Karvonen T, Oates CJ (2023) Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed. Journal of Machine Learning Research, 24(120):1−47.

[Journal] [arXiv]

Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres

Hubbert S, Porcu E, Oates CJ, Girolami M (2023) Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Transactions on Machine Learning Research.

[OpenReview] [arXiv]

Meta-learning Control Variates: Variance Reduction with Limited Data
Meta-learning Control Variates: Variance Reduction with Limited Data

Sun Z, Oates CJ, Briol FX. Meta-learning Control Variates: Variance Reduction with Limited Data. Conference on Uncertainty in Artificial Intelligence (UAI 2023)

[arXiv]

Selected for oral presentation.

Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators

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, 19(6): e1011257. [Journal]

Generalised Bayesian Inference for Discrete Intractable Likelihood
Generalised Bayesian Inference for Discrete Intractable Likelihood

Matsubara T, Knoblauch J, Briol FX, Oates CJ. (2023) Generalised Bayesian Inference for Discrete Intractable Likelihood. Journal of the American Statistical Society, 119(547), 2345–2355. [Journal][arXiv]

Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG

Reid TW, Ipsen ICF, Cockaynne J, Oates CJ. Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. Numerische Mathematik, 155, 239-288. [Journal] [arXiv]

Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting
Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting

Oates CJ. Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting. SIAM Review, 65(3):905-915. [Journal]

2022

Featured
Robust Generalised Bayesian Inference for Intractable Likelihoods
Robust Generalised Bayesian Inference for Intractable Likelihoods

Matsubara T, Knoblauch J, Briol FX, Oates CJ. Robust Generalised Bayesian Inference for Intractable Likelihoods. Journal of the Royal Statistical Society (Series B), 84(3):997-1022.

[Journal] [arXiv] [Video]

ISBA 2021 Best Student/Postdoc Contributed Paper Award

Best Student Paper Award, ASA Section on Bayesian Statistical Science, 2022

This work has been presented as a conference abstract at the NeurIPS 2021 Workshop “Your Model is Wrong: Robustness and Misspecification in Probabilistic Modeling”. [Web]

Optimal Thinning of MCMC Output
Optimal Thinning of MCMC Output

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), 84(4):1059-1081.

[Journal] [arXiv] [Software] [Blog1] [Blog2] [Video]

This work has been presented as a conference abstract at the Third Symposium on Advances in Approximate Bayesian Inference (AABI 2020). [Web] [Video]

Semi-Exact Control Functionals From Sard's Method
Semi-Exact Control Functionals From Sard's Method

South LF, Karvonen T, Nemeth C, Girolami M, Oates CJ. Semi-Exact Control Functionals From Sard's Method. Biometrika, 109(2):351–367.

[Journal] [arXiv] [Software]

Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization

Si S, Oates CJ, Duncan AB, Carin L, Briol F-X. Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. Proceedings of the 14th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing, Springer 2022.

[Book] [arXiv] [Video]

Post-Processing of MCMC
Post-Processing of MCMC

South LF, Riabiz M, Teymur O, Oates CJ. Post-Processing of MCMC. Annual Reviews of Statistics and its Application, 9:529-555.

[Journal] [arXiv]

A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel
A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel

Oates CJ, Kendall WS, Fleming L. A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel. Technometrics, 64(3):370-383.

[Journal] [arXiv]

This work has been presented as a conference abstract: Oates CJ, Kendall WS, Fleming L. Generative Modelling of Rough Surfaces: An Application to 3D-Printed Stainless Steel. NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation, and Design. [Web]

A Riemann--Stein Kernel Method
A Riemann--Stein Kernel Method

Barp A, Oates CJ, Porcu E, Girolami M. A Riemann--Stein Kernel Method. Bernoulli, 28(4): 2181-2208.

[Journal] [arXiv]

Testing Whether a Learning Procedure is Calibrated
Testing Whether a Learning Procedure is Calibrated

Cockayne J, Graham MM, Oates CJ, Sullivan TJ. (2022) Testing Whether a Learning Procedure is Calibrated. Journal of Machine Learning Research, 23(203):1-36.

[Journal] [arXiv]

Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators
Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators

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 Proceedings of the 10th Vienna International Conference on Mathematical Modelling, 2022.

[Web]

2021

Featured
A Data-Centric Approach to Generative Modelling for 3D-Printed Steel
A Data-Centric Approach to Generative Modelling for 3D-Printed Steel

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, 477(2255).

[Journal]

Probabilistic Iterative Methods for Linear Systems
Probabilistic Iterative Methods for Linear Systems

Cockayne J, Ipsen ICF, Oates CJ, Reid TW. Probabilistic Iterative Methods for Linear Systems. Journal of Machine Learning Research, 22(232):1-34.

[Journal] [arXiv]

Bayesian Numerical Methods for Nonlinear Partial Differential Equations
Bayesian Numerical Methods for Nonlinear Partial Differential Equations

Wang J, Cockayne J, Chkrebtii O, Sullivan TJ, Oates CJ. Bayesian Numerical Methods for Nonlinear Partial Differential Equations. Statistics and Computing, 31(55).

[Journal] [arXiv]

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks

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, 22(157):1−57.

[Journal] [arXiv] [Video]

Integration in Reproducing Kernel Hilbert Spaces of Gaussian Kernels
Integration in Reproducing Kernel Hilbert Spaces of Gaussian Kernels

Karvonen T, Oates CJ, Girolami M. Integration in Reproducing Kernel Hilbert Spaces of Gaussian Kernels. Mathematics of Computation, 90(331):2209-2233.

[Journal] [arXiv]

Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy

Teymur O, Gorham J, Riabiz M, Oates CJ. Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. International Conference on Artificial Intelligence and Statistics (AISTATS 2021)

[Journal] [arXiv]

Causal Graphical Models for Systems-Level Engineering Assessment
Causal Graphical Models for Systems-Level Engineering Assessment

Stephenson V, Oates CJ, Finlayson A, Thomas C, Wilson K. Causal Graphical Models for Systems-Level Engineering Assessment. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(2):04021011.

[Journal] [Preprint]

Improved Calibration of Numerical Integration Error in Sigma-Point Filters
Improved Calibration of Numerical Integration Error in Sigma-Point Filters

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, 66(3):1286-1292.

[Journal] [arXiv]

Measure Transport with Kernel Stein Discrepancy
Measure Transport with Kernel Stein Discrepancy

Fisher MA, Nolan T, Graham MM, Prangle D, Oates CJ. Measure Transport with Kernel Stein Discrepancy, AISTATS 2021.

[Journal] [arXiv] [Software]

Selected or oral presentation (top 3%)

(Note that the arXiv version corrects errors in the AISTATS version.)

Black Box Probabilistic Numerics
Black Box Probabilistic Numerics

Teymur O, Foley CN, Breen PG, Karvonen T, Oates CJ. Black Box Probabilistic Numerics. Advances in Neural Information Processing Systems (NeurIPS 2021).

[Journal] [arXiv]

GaussED: A Probabilistic Programming Language for Sequential Experimental Design
GaussED: A Probabilistic Programming Language for Sequential Experimental Design

Fisher MA, Teymur O, Oates CJ. GaussED: A Probabilistic Programming Language for Sequential Experimental Design. Newcastle University Technical Report. [arXiv]

2020

Featured
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions

Karvonen T, Wynne G, Tronarp F, Oates CJ, Särkkä S. Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. SIAM Journal of Uncertainty Quantification, 8(3):926-958.

[Journal] [arXiv] [Video]

A Locally Adaptive Bayesian Cubature Method
A Locally Adaptive Bayesian Cubature Method

Fisher MA, Oates CJ, Powell C, Teckentrup A. A Locally Adaptive Bayesian Cubature Method. International Conference on Artificial Intelligence and Statistics (AISTATS 2020).

[Journal] [arXiv] [Video] [Video2]

Discussion of “Unbiased Markov Chain Monte Carlo with Couplings“
Discussion of “Unbiased Markov Chain Monte Carlo with Couplings“

South LF, Nemeth C, Oates CJ. Discussion of “Unbiased Markov Chain Monte Carlo with Couplings“. Journal of the Royal Statistical Society (Series B), 82(3):590-592.

[Journal] [arXiv]

Optimality Criteria for Probabilistic Numerical Methods
Optimality Criteria for Probabilistic Numerical Methods

Oates CJ, Cockayne J, Prangle D, Sullivan TJ, Girolami M. Optimality Criteria for Probabilistic Numerical Methods. In Multivariate Algorithms and Information-Based Complexity, eds, Hickernell, Kritzer, Berlin/Boston De Gruyter.

[Book] [arXiv] [Workshop]

A Role for Symmetry in the Bayesian Solution of Differential Equations
A Role for Symmetry in the Bayesian Solution of Differential Equations

Wang J, Cockayne J, Oates CJ. A Role for Symmetry in the Bayesian Solution of Differential Equations. Bayesian Analysis, 15(4):1057-1085.

[Journal] [arXiv]

2019

Featured
Editorial: Special Edition on Probabilistic Numerics
Editorial: Special Edition on Probabilistic Numerics

Girolami M, Ipsen I, Oates CJ, Owen A, Sullivan T. Editorial: Special Edition on Probabilistic Numerics. Statistics and Computing, 29(6):1181-1183.

[Journal]

Causal Learning via Manifold Regularization
Causal Learning via Manifold Regularization

Hill SM, Oates CJ, Blythe D, Mukherjee S. Causal Learning via Manifold Regularization. Journal of Machine Learning Research, 20:1-32.

[Journal] [arXiv]

Stein Point Markov Chain Monte Carlo
Stein Point Markov Chain Monte Carlo

Chen WY, Barp A, Briol FX, Gorham J, Girolami M, Mackey L, Oates CJ. Stein Point Markov Chain Monte Carlo. International Conference on Machine Learning (ICML 2019).

[Journal] [Supplement] [arXiv] [Software] [Video]

A Modern Retrospective on Probabilistic Numerics
A Modern Retrospective on Probabilistic Numerics

Oates CJ, Sullivan TJ. A Modern Retrospective on Probabilistic Numerics. Statistics and Computing, 29(6):1335-1351.

[Journal] [arXiv]

Bayesian Probabilistic Numerical Methods
Bayesian Probabilistic Numerical Methods

Cockayne J, Oates CJ, Sullivan T, Girolami M. Bayesian Probabilistic Numerical Methods. SIAM Review, 61(4):756-789.

[Journal] [arXiv] [Video1] [Video2] [Video3] [Blog]

Best Student Paper Prize, ASA Section on Bayesian Statistical Science

Featured on the cover of the journal

Highly Cited

Symmetry Exploits for Bayesian Cubature Methods
Symmetry Exploits for Bayesian Cubature Methods

Karvonen T, Särkkä S, Oates CJ. Symmetry Exploits for Bayesian Cubature Methods. Statistics and Computing, 29:1231-1248.

[Journal] [arXiv] [Software]

A Bayesian Conjugate Gradient Method
A Bayesian Conjugate Gradient Method

Cockayne J, Oates CJ, Ipsen I, Girolami M. A Bayesian Conjugate Gradient Method (with discussion and rejoinder). Bayesian Analysis, 14(3):937-1012.

[Journal] [arXiv] [Software] [Webinar]

This was the first ever discussion paper webinar held by the journal.

Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment
Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment

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, 114(528):1518-1531.

[Journal] [arXiv] [Software]

Optimal Monte Carlo Integration on Closed Manifolds
Optimal Monte Carlo Integration on Closed Manifolds

Ehler M, Gräf M, Oates CJ. Optimal Monte Carlo Integration on Closed Manifolds. Statistics and Computing, 29(6):1203-1214.

[Journal] [arXiv]

Convergence Rates for a Class of Estimators Based on Stein's Method
Convergence Rates for a Class of Estimators Based on Stein's Method

Oates CJ, Cockayne J, Briol F-X, Girolami M. (2019) Convergence Rates for a Class of Estimators Based on Stein's Method. Bernoulli, 25(2):1141-1159.

[Journal] [arXiv] [Stein]

Probabilistic Integration: A Role in Statistical Computation? (with discussion and rejoinder)
Probabilistic Integration: A Role in Statistical Computation? (with discussion and rejoinder)

Briol F-X, Oates, CJ, Girolami, M, Osborne, MA, Sejdinovic, D. Probabilistic Integration: A Role in Statistical Computation? (with discussion and rejoinder) Statistical Science, 34(1):1-22. (Rejoinder on p38-42.)

[Journal] [Discussion1] [Discussion2] [Discussion3] [Rejoinder] [arXiv] [Video] [Poster] [Blog1] [Blog2] [Blog3] [Blog4] [Blog5] [ProbNum]

Best Student Paper Prize, ASA Section on Bayesian Statistical Science

Graphical Models in Molecular Systems Biology
Graphical Models in Molecular Systems Biology

Mukherjee S, Oates CJ. Graphical Models in Molecular Systems Biology. In Handbook of Graphical Models, eds. Maathuis M, Drton M, Lauritzen S, Wainwright M, CRC Press.

[Publisher] [Preprint]

2018

Featured
A Bayes-Sard Cubature Method
A Bayes-Sard Cubature Method

Karvonen T, Oates CJ, Särkkä S. A Bayes-Sard Cubature Method. Advances in Neural Information Processing Systems (NeurIPS 2018).

[Journal] [arXiv]

Stein Points
Stein Points

Chen WY, Mackey L, Gorham J, Briol FX, Oates CJ. Stein Points. International Conference on Machine Learning (ICML 2018), Proceedings of Machine Learning Research, 80:844-853.

[Journal] [arXiv]

2017

Featured
Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models
Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models

Oates CJ, Niederer S, Lee A, Briol F-X, Girolami M. Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models. Advances in Neural Information Processing Systems (NIPS 2017).

[Journal] [arXiv] [Video] [Poster] [Blog]

On the Sampling Problem for Kernel Quadrature
On the Sampling Problem for Kernel Quadrature

Briol FX, Oates CJ, Cockayne J, Chen, WY, Girolami M. (2017) On the Sampling Problem for Kernel Quadrature. International Conference on Machine Learning (ICML 2017), Proceedings of Machine Learning Research, 70:586-595.

[Journal] [arXiv]

Discussion of "A Bayesian information criterion for singular models"
Discussion of "A Bayesian information criterion for singular models"

Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Discussion of "A Bayesian information criterion for singular models". Journal of the Royal Statistical Society (Series B), 79(2):323-380.

[Journal] [arXiv]

Repair of Partly Misspecified Causal Diagrams
Repair of Partly Misspecified Causal Diagrams

Oates CJ, Kasza J, Simpson JA, Forbes AB. (2017) Repair of Partly Misspecified Causal Diagrams. Epidemiology, 28(4):548-552.

[Journal] [PubMed] [Software] [Video] [Erratum]

Control Functionals for Monte Carlo Integration
Control Functionals for Monte Carlo Integration

Oates CJ, Girolami M, Chopin N. (2017) Control Functionals for Monte Carlo Integration. Journal of the Royal Statistical Society, Series B, 79(3):695-718.

[Journal] [arXiv] [Blog1] [Blog2] [Supplement] [Software]

Investigation of the Widely Applicable Bayesian Information Criteria
Investigation of the Widely Applicable Bayesian Information Criteria

Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Investigation of the Widely Applicable Bayesian Information Criteria. Statistics and Computing, 27(3):833-844.

[Journal] [arXiv] [Blog]

2016

Featured
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems

Cockayne J, Oates CJ, Sullivan T, Girolami M (2016) Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Ed. Geert Verdoolaege, AIP Conference Proceedings, 1853:060001.

[Journal] [arXiv]

This is a short form of the full paper: Cockayne J, Oates CJ, Sullivan T, Girolami M. Probabilistic Meshless Methods for Bayesian Inverse Problems.

[arXiv] [Video] [Poster] [ProbNum] [Video]

Discussion of “Causal inference using invariant prediction: identification and confidence intervals”
Discussion of “Causal inference using invariant prediction: identification and confidence intervals”

Oates CJ, Kasza J, Mukherjee S (2016) Discussion of “Causal inference using invariant prediction: identification and confidence intervals” by Peters, Bühlmann and Meinshausen. Journal of the Royal Statistical Society (Series B), 78(5):947-1012.

[Journal] [arXiv]

RNA editing generates sequence diversity within cell populations
RNA editing generates sequence diversity within cell populations

Harjanto D, Papamarkou T, Oates CJ, Rayon Estrada V, Papavasiliou FN, Papavasiliou A. (2016) RNA editing generates sequence diversity within cell populations. Nature Communications, 7:12145.

[Journal]

Control Functionals for Quasi-Monte Carlo Integration
Control Functionals for Quasi-Monte Carlo Integration

Oates CJ, Girolami M. (2016) Control Functionals for Quasi-Monte Carlo Integration. Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP, 51:56-65.

[Journal] [arXiv] [Poster]

Selected for Oral Presentation (top 6.5% of submissions)

Estimation of Causal Structure Using Conditional DAG Models
Estimation of Causal Structure Using Conditional DAG Models

Oates CJ, Smith JQ, Mukherjee S. (2016) Estimation of Causal Structure Using Conditional DAG Models. Journal of Machine Learning Research, 17(54):1−23.

[Journal] [arXiv] [Supplement]

The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation
The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation

Oates CJ, Papamarkou T, Girolami M (2016) The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation. Journal of the American Statistical Association, 111(514):634-645.

[Journal] [arXiv] [Blog1] [Blog2] [Blog3]

Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods

Friel N, Mira A, Oates CJ (2015) Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods. Bayesian Analysis, 11(1):215-245.

[Journal] [arXiv] [Blog1] [Blog2]

Exact Estimation of Multiple Directed Acyclic Graphs
Exact Estimation of Multiple Directed Acyclic Graphs

Oates CJ, Smith JQ, Mukherjee S, Cussens J (2016) Exact Estimation of Multiple Directed Acyclic Graphs. Statistics and Computing, 26(4):797-811.

[Journal] [arXiv] [Poster] [Software]

2015

Featured
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

Briol F-X, Oates CJ, Girolami M, Osborne MA. (2015) Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. Advances in Neural Information Processing Systems (NIPS 2015).

[Journal] [arXiv] [Video] [Blog1] [Blog2] [ProbNum]

Selected for Spotlight Presentation

Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer
Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer

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. (2015) Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer. PLoS One, 10(7):e0133219.

[Journal]

Accelerated Nonparametrics for Cascades of Poisson Processes
Accelerated Nonparametrics for Cascades of Poisson Processes

Oates CJ. (2015) Accelerated Nonparametrics for Cascades of Poisson Processes. Stat, 4(1):183-195.

[Journal] [arXiv] [Newsletter]

Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin
Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin

Oates CJ, Simpson D, Girolami M (2015) Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin. Journal of the Royal Statistical Society (Series B), 77(3):555-556.

[Journal] [arXiv] [Blog]

Towards a Multi-Subject Analysis of Neural Connectivity
Towards a Multi-Subject Analysis of Neural Connectivity

Oates CJ, Costa L, Nichols T (2015) Towards a Multi-Subject Analysis of Neural Connectivity. Neural Computation, 27:151–170.

[Journal] [arXiv]

2014

Featured
Quantifying the Multi-Scale Performance of Network Inference Algorithms
Quantifying the Multi-Scale Performance of Network Inference Algorithms

Oates CJ, Amos R, Spencer SEF (2014) Quantifying the Multi-Scale Performance of Network Inference Algorithms. Statistical Applications in Genetics and Molecular Biology 13(5):611-631.

[Journal] [arXiv] [Supplement]

Joint Estimation of Multiple Related Biological Networks
Joint Estimation of Multiple Related Biological Networks

Oates CJ, Korkola J, Gray, JW, Mukherjee S (2014) Joint Estimation of Multiple Related Biological Networks. The Annals of Applied Statistics 8(3):1892-1919.

[Journal] [arXiv]

Causal network inference using biochemical kinetics
Causal network inference using biochemical kinetics

Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW, Mukherjee S (2014) Causal network inference using biochemical kinetics. Bioinformatics 30(17):i468-i474.

[Journal] [arXiv] [Software]

Best Paper at the European Conference on Computational Biology 2014

Joint Structure Learning of Multiple Non-Exchangeable Networks
Joint Structure Learning of Multiple Non-Exchangeable Networks

Oates CJ, Mukherjee S (2014) Joint Structure Learning of Multiple Non-Exchangeable Networks. Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP 33:687-695.

[Journal] [arXiv]

Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines
Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines

Casale FP, Giurato G, Nassa G, Armond J, Oates CJ, Corà D, Gamba A, Mukherjee S, Weisz A, Nicodemi M (2014) Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS One 9(2):e88485.

[Journal]

A stochastic model dissects cellular states and heterogeneity in transition processes
A stochastic model dissects cellular states and heterogeneity in transition processes

Armond J, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M, Mukherjee S (2014) A stochastic model dissects cellular states and heterogeneity in transition processes. Nature Scientific Reports 4:3692.

[Journal] [WRAP]

2013

Featured
Bayesian Inference for Protein Signalling Networks
Bayesian Inference for Protein Signalling Networks

Oates CJ (2013) Bayesian Inference for Protein Signalling Networks. PhD Thesis.

[pdf]

Self Organisation and Emergence
Self Organisation and Emergence

Chau Y-X, Oates CJ, Rana AA, Robinson L, Nicodemi M. (2013) Self Organisation and Emergence. In: Complexity Science: The Warwick Master’s Course (London Mathematical Society Lecture Note Series). Ed. by Ball R, Kolokoltsov V, MacKay R., Cambridge University Press.

[Publisher]

2012

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Network Inference and Biological Dynamics
Network Inference and Biological Dynamics

Oates CJ, Mukherjee S (2012) Network Inference and Biological Dynamics. The Annals of Applied Statistics 6(3):1209-1235.

[Journal] [arXiv]

Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics
Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics

Oates CJ, Hennessy BT, Lu Y, Mills GB, Mukherjee S (2012) Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics. Bioinformatics 28(18):2342-2348.

[Journal] [arXiv]