| Solving high-dimensional Hamilton–Jacobi–Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space N Nüsken, L Richter Partial differential equations and applications 2 (4), 48, 2021 | 115 | 2021 |
| Solving high-dimensional parabolic PDEs using the tensor train format L Richter, L Sallandt, N Nüsken International Conference on Machine Learning, 8998-9009, 2021 | 58 | 2021 |
| Variational characterization of free energy: Theory and algorithms C Hartmann, L Richter, C Schütte, W Zhang Entropy 19 (11), 626, 2017 | 55 | 2017 |
| An optimal control perspective on diffusion-based generative modeling J Berner, L Richter, K Ullrich arXiv preprint arXiv:2211.01364, 2022 | 50 | 2022 |
| VarGrad: a low-variance gradient estimator for variational inference L Richter, A Boustati, N Nüsken, F Ruiz, OD Akyildiz Advances in Neural Information Processing Systems 33, 13481-13492, 2020 | 42 | 2020 |
| Variational approach to rare event simulation using least-squares regression C Hartmann, O Kebiri, L Neureither, L Richter Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (6), 2019 | 34 | 2019 |
| Improved sampling via learned diffusions L Richter, J Berner arXiv preprint arXiv:2307.01198, 2023 | 29 | 2023 |
| Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems N Nüsken, L Richter arXiv preprint arXiv:2112.03749, 2021 | 23 | 2021 |
| Early crop classification via multi-modal satellite data fusion and temporal attention F Weilandt, R Behling, R Goncalves, A Madadi, L Richter, T Sanona, ... Remote Sensing 15 (3), 799, 2023 | 20 | 2023 |
| Robust SDE-based variational formulations for solving linear PDEs via deep learning L Richter, J Berner International Conference on Machine Learning, 18649-18666, 2022 | 19 | 2022 |
| Nonasymptotic bounds for suboptimal importance sampling C Hartmann, L Richter SIAM/ASA Journal on Uncertainty Quantification 12 (2), 309-346, 2024 | 14 | 2024 |
| Solving high-dimensional PDEs, approximation of path space measures and importance sampling of diffusions L Richter BTU Cottbus-Senftenberg, 2021 | 12 | 2021 |
| Improving control based importance sampling strategies for metastable diffusions via adapted metadynamics E Ribera Borrell, J Quer, L Richter, C Schütte SIAM Journal on Scientific Computing 46 (2), S298-S323, 2024 | 8 | 2024 |
| Error bounds for model reduction of feedback-controlled linear stochastic dynamics on Hilbert spaces S Becker, C Hartmann, M Redmann, L Richter Stochastic Processes and their Applications 149, 107-141, 2022 | 7* | 2022 |
| Dynamical measure transport and neural pde solvers for sampling J Sun, J Berner, L Richter, M Zeinhofer, J Müller, K Azizzadenesheli, ... arXiv preprint arXiv:2407.07873, 2024 | 4 | 2024 |
| Fast and unified path gradient estimators for normalizing flows L Vaitl, L Winkler, L Richter, P Kessel arXiv preprint arXiv:2403.15881, 2024 | 3 | 2024 |
| From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs L Richter, L Sallandt, N Nüsken Journal of Machine Learning Research 25, 1-40, 2024 | 2 | 2024 |
| Model order reduction for (stochastic-) delay equations with error bounds S Becker, L Richter arXiv preprint arXiv:2008.12288, 2020 | 2 | 2020 |
| Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models L Winkler, L Richter, M Opper arXiv preprint arXiv:2405.03549, 2024 | 1 | 2024 |
| Transgressing the Boundaries: Towards a Rigorous Understanding of Deep Learning and Its (Non-)Robustness C Hartmann, L Richter AI-Limits and Prospects of Artificial Intelligence 4, 43, 2023 | 1* | 2023 |
Nikolas NüskenKing's College LondonVerified email at kcl.ac.uk
