Here is a list of my academic publications, see my Google Scholar page for further details.
You can also read about some highlights from my research
αβ indicates that the authors are listed alphabetically

Submitted publications and preprints

  • O. J. Sutton, Q. Zhou, W. Wang, D. J. Higham, A. N. Gorban, A. Bastounis, I. Y. Tyukin (2024). Stealth edits for provably fixing or attacking large language models. arXiv 2406.1267 [preprint] [discussion]
  • I. Y. Tyukin, T. Tyukina, D. van Helden, Z. Zhang, E. M. Mirkes, O. J. Sutton, Q. Zhou, A. N. Gorban, P. Allison (2024). Weakly supervised learners for correction of AI errors with provable performance guarantees. arXiv 2402.00899 [preprint]
  • O. J. Sutton, Q. Zhou, I. Y. Tyukin, A. N. Gorban, A. Bastounis, D. J. Higham (2023). How adversarial attacks can disrupt seemingly stable accurate classifiers. arXiv 2309.03665 [preprint] [discussion]
  • O. J. Sutton, A. N. Gorban and I. Y. Tyukin (2022). Towards a mathematical understanding of learning from few examples with nonlinear feature maps. arXiv 2211.03607 [preprint] [discussion]
  • αβ P. Houston, M. E. Hubbard, T. J. Radley, O. J. Sutton and R. S. J. Widdowson (2022). Efficient high-order space-angle-energy polytopic discontinuous Galerkin finite element methods for linear Boltzmann transport. arXiv 2304.09592 [preprint] [discussion]

Peer reviewed publications

  • I. Y. Tyukin, T. Tyukina, D. van Helden, Z. Zhang, E. M. Mirkes, O. J. Sutton, Q. Zhou, A. N. Gorban, P. Allison (2024). Coping with AI errors with provable guarantees. Information Sciences 68(120856) [published]
  • αβ A. Bastounis, A. N. Gorban, A. C. Hansen, D. J. Higham, D. Prokhorov, O. J. Sutton, I. Y. Tyukin and Q. Zhou (2023). The boundaries of verifiable accuracy, robustness, and generalisation in deep learning. International Conference on Artificial Neural Networks (ICANN) 2023 vol 14254, pp 530-541, Springer, Cham. [preprint] [published] [discussion]
  • O. J. Sutton, Q. Zhou, A. N. Gorban, I. Y. Tyukin (2023). Relative intrinsic dimensionality is intrinsic to learning. International Conference on Artificial Neural Networks (ICANN) 2023 vol 14254, pp 516-529, Springer, Cham. [preprint] [published]
  • O. J. Sutton, A. N. Gorban and I. Y. Tyukin (2023). A geometric view on the role of nonlinear feature maps in few-shot learning. Geometric Science of Information (GSI) 2023 vol 14071, pp 560-568, Springer, Cham. [published] [discussion]
  • I. Y. Tyukin, O. J. Sutton, A. N. Gorban (2023). Learning from few examples with nonlinear feature maps. Science and Information Conference (2023) vol 711, pp 210-225, Springer, Cham. [published] [discussion] - Winner of best paper award 🏆.
  • Q. Zhou, O. J. Sutton, Y.-D. Zhang, A. N. Gorban, V. A. Makarov and I. Y. Tyukin (2023). Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data. International Joint Conference on Neural Networks (IJCNN) 2023 IEEE [published]
  • αβ Z. Dong, L. Mascotto and O. J. Sutton (2021). Residual-based a posteriori error estimates for hp-discontinuous Galerkin discretisations of the biharmonic problem. SIAM Journal on Numerical Analysis 59 (3) 1273-1298 [preprint] [published]
  • αβ A. Cangiani, E. H. Georgoulis, and O. J. Sutton (2021). Adaptive non-hierarchical Galerkin methods for parabolic problems with application to moving mesh and virtual element methods. Mathematical Models and Methods in Applied Sciences 31 (4) 711-751 [preprint] [published] [discussion]
  • O. J. Sutton (2020). Long-time L∞(L2) a posteriori error estimates for fully discrete parabolic problems. IMA Journal of Numerical Analysis Volume 40, Issue 1, 498-529 [preprint] [published]
  • αβ A. Cangiani, E. H. Georgoulis, A. Yu. Morozov, and O. J. Sutton (2018). Revealing new dynamical patterns in a reaction-diffusion model with cyclic competition via a novel computational framework. Proceedings of the Royal Society A 474 20170608 [preprint] [published] [discussion] - Featured on the journal front cover
  • O. J. Sutton (2017). The virtual element method in 50 lines of MATLAB. Numerical Algorithms 75(4), 1141-1159 [preprint] [published] [discussion] - The full code is available on GitHub
  • αβ A. Cangiani, E. H. Georgoulis, T. Pryer, and O. J. Sutton (2017). A posteriori error estimates for the virtual element method. Numerische Mathematik 137(4), 857-893 [preprint] [published] [discussion]
  • αβ A. Cangiani, G. Manzini, and O. J. Sutton (2017). Conforming and nonconforming virtual element methods for elliptic problems. IMA Journal of Numerical Analysis 37(3), 1317-1354 [preprint] [published]

Book Chapters

  • A. Cangiani, O. J. Sutton, V. Gyrya, and G. Manzini (2017). Virtual element methods for elliptic problems on polygonal meshes. K. Hormann and N. Sukumar, ed., Generalized Barycentric Coordinates in Computer Graphics and Computational Mechanics 1st ed. CRC Press [published]

Other publications

  • O. J. Sutton (2017). Virtual element methods. PhD Thesis, University of Leicester
  • αβ A. Cangiani, G. Manzini, and O. J. Sutton (2014). The Conforming Virtual Element Method for the convection-diffusion-reaction equation with variable coefficients. Technical report LA-UR-14-27710, Los Alamos National Laboratory
  • αβ A. Cangiani, G. Manzini, and O. J. Sutton (2014). Numerical results using the conforming VEM for the convection-diffusion-reaction equation with variable coefficients. Technical report LA-UR-14-27709, Los Alamos National Laboratory