| On the geometry of Stein variational gradient descent A Duncan, N Nüsken, L Szpruch Journal of Machine Learning Research 24, 1-39, 2023 | 112 | 2023 |
| 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 | 98 | 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 | 82 | 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 | 56* | 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 | 51 | 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 | 45 | 2017 |
| 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 | 35 | 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 | 30* | 2019 |
| 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 | 28 | 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 | 21 | 2021 |
| Stein variational gradient descent: Many-particle and long-time asymptotics N Nüsken, DRM Renger Foundations of Data Science 5 (3), 286-320, 2023 | 19 | 2023 |
| State and parameter estimation from observed signal increments N Nüsken, S Reich, PJ Rozdeba Entropy 21 (5), 505, 2019 | 14 | 2019 |
| 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 | 12 | 2019 |
| Rough McKean–Vlasov dynamics for robust ensemble Kalman filtering M Coghi, T Nilssen, N Nüsken, S Reich The Annals of Applied Probability 33 (6B), 5693-5752, 2023 | 11 | 2023 |
| Transport meets Variational Inference: Controlled Monte Carlo Diffusions F Vargas, S Padhy, D Blessing, N Nüsken arXiv preprint arXiv:2307.01050, 2023 | 10* | 2023 |
| Measure transport with kernel mean embeddings L Wang, N Nüsken arXiv preprint arXiv:2401.12967, 2024 | 2 | 2024 |
| Supplement to “Hypocoercivity of piecewise deterministic Markov process-Monte Carlo.” C Andrieu, A Durmus, N Nüsken, J Roussel | 2 | 2021 |
| From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs L Richter, L Sallandt, N Nüsken arXiv preprint arXiv:2307.15496, 2023 | 1 | 2023 |
| Transport, VI, and Diffusions F Vargas, N Nüsken ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023 | 1 | 2023 |
| Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm S Livingstone, N Nüsken, G Vasdekis, RY Zhang arXiv preprint arXiv:2405.14373, 2024 | | 2024 |
