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Nikolas Nüsken
Nikolas Nüsken
Verified email at uni-potsdam.de - Homepage
Title
Cited by
Cited by
Year
On the geometry of Stein variational gradient descent
A Duncan, N Nüsken, L Szpruch
arXiv preprint arXiv:1912.00894, 2019
542019
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), 1-48, 2021
532021
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
442021
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
372020
Using perturbed underdamped Langevin dynamics to efficiently sample from probability distributions
AB Duncan, N Nüsken, GA Pavliotis
Journal of Statistical Physics 169 (6), 1098-1131, 2017
362017
Solving high-dimensional parabolic PDEs using the tensor train format
L Richter, L Sallandt, N Nüsken
International Conference on Machine Learning, 8998-9009, 2021
232021
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
172019
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
112020
Constructing sampling schemes via coupling: Markov semigroups and optimal transport
N Nüsken, GA Pavliotis
arXiv preprint arXiv:1806.11026, 2018
112018
State and parameter estimation from observed signal increments
N Nüsken, S Reich, PJ Rozdeba
Entropy 21 (5), 505, 2019
102019
Stein variational gradient descent: many-particle and long-time asymptotics
N Nüsken, DR Renger
arXiv preprint arXiv:2102.12956, 2021
92021
Interpolating between BSDEs and PINNs--deep learning for elliptic and parabolic boundary value problems
N Nüsken, L Richter
arXiv preprint arXiv:2112.03749, 2021
82021
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
72018
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
52019
Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering
M Coghi, T Nilssen, N Nüsken
arXiv preprint arXiv:2107.06621, 2021
42021
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
22019
Bayesian Learning via Neural Schr\" odinger-F\" ollmer Flows
F Vargas, A Ovsianas, D Fernandes, M Girolami, ND Lawrence, N Nüsken
arXiv preprint arXiv:2111.10510, 2021
12021
Bayesian learning via neural Schrödinger–Föllmer flows
F Vargas, A Ovsianas, D Fernandes, M Girolami, ND Lawrence, N Nüsken
Statistics and Computing 33 (1), 1-22, 2023
2023
Topics in sampling schemes based on Markov processes
N Nüsken
Imperial College London, 2018
2018
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Articles 1–19