| On the geometry of Stein variational gradient descent A Duncan, N Nüsken, L Szpruch arXiv preprint arXiv:1912.00894, 2019 | 83 | 2019 |
| 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, 1-48, 2021 | 75 | 2021 |
| Affine invariant interacting Langevin dynamics for Bayesian inference A Garbuno-Inigo, N Nüsken, S Reich SIAM Journal on Applied Dynamical Systems 19 (3), 1633-1658, 2020 | 58 | 2020 |
| Hypocoercivity of piecewise deterministic Markov process-Monte Carlo C Andrieu, A Durmus, N Nüsken, J Roussel The Annals of Applied Probability 31 (5), 2478-2517, 2021 | 48 | 2021 |
| Using perturbed underdamped Langevin dynamics to efficiently sample from probability distributions AB Duncan, N Nüsken, GA Pavliotis Journal of Statistical Physics 169, 1098-1131, 2017 | 43 | 2017 |
| Solving high-dimensional parabolic PDEs using the tensor train format L Richter, L Sallandt, N Nüsken International Conference on Machine Learning, 8998-9009, 2021 | 39 | 2021 |
| 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 | 21 | 2020 |
| Note on interacting Langevin diffusions: gradient structure and ensemble Kalman sampler by Garbuno-Inigo, Hoffmann, Li and Stuart N Nüsken, S Reich arXiv preprint arXiv:1908.10890, 2019 | 20 | 2019 |
| Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems N Nüsken, L Richter arXiv preprint arXiv:2112.03749, 2021 | 15 | 2021 |
| Stein variational gradient descent: many-particle and long-time asymptotics N Nüsken, DR Renger arXiv preprint arXiv:2102.12956, 2021 | 12 | 2021 |
| State and parameter estimation from observed signal increments N Nüsken, S Reich, PJ Rozdeba Entropy 21 (5), 505, 2019 | 12 | 2019 |
| Constructing sampling schemes via coupling: Markov semigroups and optimal transport N Nüsken, GA Pavliotis arXiv preprint arXiv:1806.11026, 2018 | 12 | 2018 |
| Bayesian learning via neural Schrödinger–Föllmer flows F Vargas, A Ovsianas, D Fernandes, M Girolami, ND Lawrence, N Nüsken Statistics and Computing 33 (1), 3, 2023 | 10 | 2023 |
| Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering M Coghi, T Nilssen, N Nüsken, S Reich arXiv preprint arXiv:2107.06621, 2021 | 7 | 2021 |
| Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo. arXiv e-prints, page C Andrieu, A Durmus, N Nüsken, J Roussel arXiv preprint arXiv:1808.08592, 2018 | 7 | 2018 |
| Note on interacting Langevin diffusions: gradient structure and ensemble Kalman sampler by Garbuno-Inigo N Nüsken, S Reich Hoffmann, Li and Stuart. arXiv e-prints 1908, 2019 | 5 | 2019 |
| Stein variational gradient descent: Many-particle and long-time asymptotics N Nüsken, DRM Renger Foundations of Data Science 5 (3), 286-320, 2023 | 2 | 2023 |
| Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schr\" odinger Bridges F Vargas, N Nüsken arXiv preprint arXiv:2307.01050, 2023 | 2 | 2023 |
| Constructing sampling schemes via coupling: Markov semigroups and optimal transport N Nusken, GA Pavliotis SIAM/ASA Journal on Uncertainty Quantification 7 (1), 324-382, 2019 | 2 | 2019 |
| On the geometry of Stein variational gradient descent N Nüsken Journal of Machine Learning Research 24, 1-39, 2023 | 1 | 2023 |
