| Kernels for sequentially ordered data FJ Király, H Oberhauser Journal of Machine Learning Research 20 (31), 1-45, 2019 | 162 | 2019 |
| Signature Moments to Characterize Laws of Stochastic Processes I Chevyrev, H Oberhauser Journal of Machine Learning Research 23 (176), 1-42, 2022 | 137 | 2022 |
| A (rough) pathwise approach to a class of non-linear stochastic partial differential equations M Caruana, PK Friz, H Oberhauser Annales de l'Institut Henri Poincaré C, Analyse non linéaire 28 (1), 27-46, 2011 | 108 | 2011 |
| Robust filtering: correlated noise and multidimensional observation D Crisan, J Diehl, PK Friz, H Oberhauser The Annals of Applied Probability 23 (5), 2139-2160, 2013 | 78 | 2013 |
| Persistence paths and signature features in topological data analysis I Chevyrev, V Nanda, H Oberhauser IEEE transactions on pattern analysis and machine intelligence 42 (1), 192-202, 2018 | 77 | 2018 |
| Rough path limits of the Wong–Zakai type with a modified drift term P Friz, H Oberhauser Journal of Functional Analysis 256 (10), 3236-3256, 2009 | 67 | 2009 |
| A feature set for streams and an application to high-frequency financial tick data T Lyons, H Ni, H Oberhauser Proceedings of the 2014 International Conference on Big Data Science and …, 2014 | 59 | 2014 |
| Bayesian learning from sequential data using gaussian processes with signature covariances C Toth, H Oberhauser International Conference on Machine Learning, 9548-9560, 2020 | 56* | 2020 |
| Rough path stability of (semi-) linear SPDEs P Friz, H Oberhauser Probability Theory and Related Fields 158 (1), 401-434, 2014 | 47* | 2014 |
| A Lévy area between Brownian motion and rough paths with applications to robust nonlinear filtering and rough partial differential equations J Diehl, H Oberhauser, S Riedel Stochastic processes and their applications 125 (1), 161-181, 2015 | 44 | 2015 |
| A generalized Fernique theorem and applications P Friz, H Oberhauser Proceedings of the American Mathematical Society 138 (10), 3679-3688, 2010 | 40 | 2010 |
| An optimal polynomial approximation of Brownian motion J Foster, T Lyons, H Oberhauser SIAM Journal on Numerical Analysis 58 (3), 1393-1421, 2020 | 36 | 2020 |
| Root’s barrier, viscosity solutions of obstacle problems and reflected FBSDEs P Gassiat, H Oberhauser, G Dos Reis Stochastic Processes and their Applications 125 (12), 4601-4631, 2015 | 35* | 2015 |
| On the splitting-up method for rough (partial) differential equations P Friz, H Oberhauser Journal of Differential Equations 251 (2), 316-338, 2011 | 31 | 2011 |
| Adapted topologies and higher rank signatures P Bonnier, C Liu, H Oberhauser The Annals of Applied Probability 33 (3), 2136-2175, 2023 | 26 | 2023 |
| The shifted ODE method for underdamped Langevin MCMC J Foster, T Lyons, H Oberhauser arXiv preprint arXiv:2101.03446, 2021 | 24 | 2021 |
| The functional Itō formula under the family of continuous semimartingale measures H Oberhauser Stochastics and Dynamics 16 (04), 1650010, 2016 | 24* | 2016 |
| Positively weighted kernel quadrature via subsampling S Hayakawa, H Oberhauser, T Lyons Advances in Neural Information Processing Systems 35, 6886-6900, 2022 | 23 | 2022 |
| Nonlinear independent component analysis for discrete-time and continuous-time signals A Schell, H Oberhauser Annals of Statistics, 2023 | 21* | 2023 |
| Probabilistic supervised learning F Gressmann, FJ Király, B Mateen, H Oberhauser arXiv preprint arXiv:1801.00753, 2018 | 18 | 2018 |
