| Synthetic Data--what, why and how? J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ... arXiv preprint arXiv:2205.03257, 2022 | 418 | 2022 |
| Conditional sig-wasserstein gans for time series generation S Liao, H Ni, L Szpruch, M Wiese, M Sabate-Vidales, B Xiao arXiv preprint arXiv:2006.05421, 2020 | 230 | 2020 |
| Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients X Mao, L Szpruch Journal of Computational and Applied Mathematics 238, 14-28, 2013 | 212 | 2013 |
| An Euler-type method for the strong approximation of the Cox–Ingersoll–Ross process S Dereich, A Neuenkirch, L Szpruch Proceedings of the royal society A: mathematical, physical and engineering …, 2012 | 204 | 2012 |
| First order strong approximations of scalar SDEs defined in a domain A Neuenkirch, L Szpruch Numerische Mathematik 128 (1), 103-136, 2014 | 185 | 2014 |
| Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms A Koshiyama, E Kazim, P Treleaven, P Rai, L Szpruch, G Pavey, ... Royal Society Open Science 11 (5), 230859, 2024 | 177 | 2024 |
| Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without Lévy area simulation MB Giles, L Szpruch | 174 | 2014 |
| Mean-field Langevin dynamics and energy landscape of neural networks K Hu, Z Ren, D Šiška, Ł Szpruch Annales de l'Institut Henri Poincare (B) Probabilites et statistiques 57 (4 …, 2021 | 166 | 2021 |
| Almost sure exponential stability of numerical solutions for stochastic delay differential equations F Wu, X Mao, L Szpruch Numerische Mathematik 115 (4), 681-697, 2010 | 162 | 2010 |
| On the geometry of Stein variational gradient descent A Duncan, N Nüsken, L Szpruch Journal of Machine Learning Research 24 (56), 1-39, 2023 | 154 | 2023 |
| Strong convergence rates for backward Euler–Maruyama method for non-linear dissipative-type stochastic differential equations with super-linear diffusion coefficients X Mao, L Szpruch Stochastics An International Journal of Probability and Stochastic Processes …, 2013 | 146 | 2013 |
| McKean–Vlasov SDEs under measure dependent Lyapunov conditions WRP Hammersley, D Šiška, Ł Szpruch | 144 | 2021 |
| Numerical simulation of a strongly nonlinear Ait-Sahalia-type interest rate model L Szpruch, X Mao, DJ Higham, J Pan BIT Numerical Mathematics 51 (2), 405-425, 2011 | 129 | 2011 |
| Sig-Wasserstein GANs for time series generation H Ni, L Szpruch, M Sabate-Vidales, B Xiao, M Wiese, S Liao Proceedings of the Second ACM International Conference on AI in Finance, 1-8, 2021 | 124 | 2021 |
| The AI revolution: Opportunities and challenges for the finance sector C Maple, L Szpruch, G Epiphaniou, K Staykova, S Singh, W Penwarden, ... arXiv preprint arXiv:2308.16538, 2023 | 113 | 2023 |
| Nonasymptotic bounds for sampling algorithms without log-concavity MB Majka, A Mijatović, Ł Szpruch | 108 | 2020 |
| Convergence, non-negativity and stability of a new Milstein scheme with applications to finance DJ Higham, X Mao, L Szpruch arXiv preprint arXiv:1204.1647, 2012 | 99 | 2012 |
| Weak quantitative propagation of chaos via differential calculus on the space of measures JF Chassagneux, L Szpruch, A Tse The Annals of Applied Probability 32 (3), 1929-1969, 2022 | 80 | 2022 |
| Sig-SDEs model for quantitative finance IP Arribas, C Salvi, L Szpruch Proceedings of the First ACM International Conference on AI in Finance, 1-8, 2020 | 80 | 2020 |
| Identifiability in inverse reinforcement learning H Cao, S Cohen, L Szpruch Advances in Neural Information Processing Systems 34, 12362-12373, 2021 | 78 | 2021 |
David SiskaSchool of Mathematics, University of EdinburghVerified email at ed.ac.uk
