| 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 | 156 | 2013 |
| Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without Lévy area simulation MB Giles, L Szpruch | 147 | 2014 |
| 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 | 145 | 2012 |
| Almost sure exponential stability of numerical solutions for stochastic delay differential equations F Wu, X Mao, L Szpruch Numerische Mathematik 115, 681-697, 2010 | 141 | 2010 |
| First order strong approximations of scalar SDEs defined in a domain A Neuenkirch, L Szpruch Numerische Mathematik 128 (1), 103-136, 2014 | 118 | 2014 |
| 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 | 118 | 2013 |
| Numerical simulation of a strongly nonlinear Ait-Sahalia-type interest rate model L Szpruch, X Mao, DJ Higham, J Pan BIT Numerical Mathematics 51, 405-425, 2011 | 85 | 2011 |
| McKean–Vlasov SDEs under measure dependent Lyapunov conditions WRP Hammersley, D Šiška, Ł Szpruch | 78 | 2021 |
| 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 | 72 | 2012 |
| On the geometry of Stein variational gradient descent A Duncan, N Nüsken, L Szpruch arXiv preprint arXiv:1912.00894, 2019 | 62 | 2019 |
| Conditional sig-wasserstein gans for time series generation H Ni, L Szpruch, M Wiese, S Liao, B Xiao arXiv preprint arXiv:2006.05421, 2020 | 61 | 2020 |
| Mean-field Langevin dynamics and energy landscape of neural networks K Hu, Z Ren, D Siska, L Szpruch arXiv preprint arXiv:1905.07769, 2019 | 56 | 2019 |
| The true cost of stochastic gradient Langevin dynamics T Nagapetyan, AB Duncan, L Hasenclever, SJ Vollmer, L Szpruch, ... arXiv preprint arXiv:1706.02692, 2017 | 51 | 2017 |
| Nonasymptotic bounds for sampling algorithms without log-concavity MB Majka, A Mijatović, Ł Szpruch | 48 | 2020 |
| An Adaptive Euler--Maruyama Scheme for Stochastic Differential Equations with Discontinuous Drift and its Convergence Analysis A Neuenkirch, M Szölgyenyi, L Szpruch SIAM Journal on Numerical Analysis 57 (1), 378-403, 2019 | 44 | 2019 |
| Time discretization of FBSDE with polynomial growth drivers and reaction–diffusion PDEs A Lionnet, G Dos Reis, L Szpruch | 44 | 2015 |
| Multilevel Monte Carlo methods for applications in finance MB Giles, L Szpruch High-Performance Computing in Finance, 197-247, 2018 | 40 | 2018 |
| Towards algorithm auditing: a survey on managing legal, ethical and technological risks of AI, ML and associated algorithms A Koshiyama, E Kazim, P Treleaven, P Rai, L Szpruch, G Pavey, ... | 37 | 2021 |
| 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 | 36 | 2022 |
| Comparing hitting time behavior of Markov jump processes and their diffusion approximations L Szpruch, DJ Higham Multiscale Modeling & Simulation 8 (2), 605-621, 2010 | 31 | 2010 |
David SiskaSchool of Mathematics, University of Edinburgh and Vega ResearchVerified email at ed.ac.uk
