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ORCIDNikolas Nüsken
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2020 – today
- 2025
[j7]Robert M. Polzin, Ilja Klebanov, Nikolas Nüsken, Péter Koltai:
Coherent Set Identification Via Direct Low Rank Maximum Likelihood Estimation. J. Nonlinear Sci. 35(1): 2 (2025)- 2024
- [j6]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. J. Mach. Learn. Res. 25: 248:1-248:40 (2024) - [c3]Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken:
Transport meets Variational Inference: Controlled Monte Carlo Diffusions. ICLR 2024 - [i16]Linfeng Wang, Nikolas Nüsken:
Measure transport with kernel mean embeddings. CoRR abs/2401.12967 (2024) - [i15]Samuel Livingstone, Nikolas Nüsken, Giorgos Vasdekis, Rui-Yang Zhang:
Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm. CoRR abs/2405.14373 (2024) - [i14]Nikolas Nüsken:
Stein transport for Bayesian inference. CoRR abs/2409.01464 (2024) - 2023
- [j5]Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch:
On the geometry of Stein variational gradient descent. J. Mach. Learn. Res. 24: 56:1-56:39 (2023) - [j4]Francisco Vargas
, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken:
Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. 33(1): 3 (2023) - [i13]Francisco Vargas, Nikolas Nüsken:
Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schrödinger Bridges. CoRR abs/2307.01050 (2023) - [i12]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. CoRR abs/2307.15496 (2023) - [i11]Robert Polzin, Ilja Klebanov, Nikolas Nüsken, Péter Koltai:
Nonnegative matrix factorization for coherent set identification by direct low rank maximum likelihood estimation. CoRR abs/2308.07663 (2023) - 2021
- [c2]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
Solving high-dimensional parabolic PDEs using the tensor train format. ICML 2021: 8998-9009 - [i10]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
Solving high-dimensional parabolic PDEs using the tensor train format. CoRR abs/2102.11830 (2021) - [i9]Nikolas Nüsken, D. R. Michiel Renger:
Stein Variational Gradient Descent: many-particle and long-time asymptotics. CoRR abs/2102.12956 (2021) - [i8]Michele Coghi, Torstein Nilssen, Nikolas Nüsken:
Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering. CoRR abs/2107.06621 (2021) - [i7]Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken:
Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR abs/2111.10510 (2021) - [i6]Nikolas Nüsken, Lorenz Richter:
Interpolating between BSDEs and PINNs - deep learning for elliptic and parabolic boundary value problems. CoRR abs/2112.03749 (2021) - 2020
- [j3]Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich:
Affine Invariant Interacting Langevin Dynamics for Bayesian Inference. SIAM J. Appl. Dyn. Syst. 19(3): 1633-1658 (2020) - [c1]Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz:
VarGrad: A Low-Variance Gradient Estimator for Variational Inference. NeurIPS 2020 - [i5]Nikolas Nüsken, Lorenz Richter:
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space. CoRR abs/2005.05409 (2020) - [i4]Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz:
VarGrad: A Low-Variance Gradient Estimator for Variational Inference. CoRR abs/2010.10436 (2020)
2010 – 2019
- 2019
- [j2]Nikolas Nüsken, Sebastian Reich, Paul J. Rozdeba:
State and Parameter Estimation from Observed Signal Increments. Entropy 21(5): 505 (2019) - [j1]Nikolas Nüsken, Grigorios A. Pavliotis:
Constructing Sampling Schemes via Coupling: Markov Semigroups and Optimal Transport. SIAM/ASA J. Uncertain. Quantification 7(1): 324-382 (2019) - [i3]Nikolas Nüsken, Sebastian Reich:
Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. CoRR abs/1908.10890 (2019) - [i2]Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch:
On the geometry of Stein variational gradient descent. CoRR abs/1912.00894 (2019) - [i1]Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich:
Affine invariant interacting Langevin dynamics for Bayesian inference. CoRR abs/1912.02859 (2019)
Coauthor Index
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