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Nikolas Nüsken
Nikolas Nüsken
在 kcl.ac.uk 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
On the geometry of Stein variational gradient descent
A Duncan, N Nüsken, L Szpruch
Journal of Machine Learning Research 24, 1-39, 2023
109*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
942021
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
772020
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
472021
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
462017
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
332020
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
31*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
252023
Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems
N Nüsken, L Richter
arXiv preprint arXiv:2112.03749, 2021
202021
Stein variational gradient descent: Many-particle and long-time asymptotics
N Nüsken, DRM Renger
Foundations of Data Science 5 (3), 286-320, 2023
192023
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
142019
State and parameter estimation from observed signal increments
N Nüsken, S Reich, PJ Rozdeba
Entropy 21 (5), 505, 2019
122019
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
102023
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
F Vargas, S Padhy, D Blessing, N Nüsken
arXiv preprint arXiv:2307.01050, 2023
9*2023
Supplement to “Hypocoercivity of piecewise deterministic Markov process-Monte Carlo.”
C Andrieu, A Durmus, N Nüsken, J Roussel
22021
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
12023
Transport, VI, and Diffusions
F Vargas, N Nüsken
ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023
12023
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
Measure transport with kernel mean embeddings
L Wang, N Nüsken
arXiv preprint arXiv:2401.12967, 2024
2024
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