Nikolas Nüsken

According to our database, Nikolas Nüsken authored at least 20 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Coherent Set Identification Via Direct Low Rank Maximum Likelihood Estimation.

Robert M. Polzin, Ilja Klebanov, Nikolas Nüsken, Péter Koltai

J. Nonlinear Sci., February, 2025

2024

From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs.

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

J. Mach. Learn. Res., 2024

Stein transport for Bayesian inference.

Nikolas Nüsken

CoRR, 2024

Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm.

Samuel Livingstone, Nikolas Nüsken, Giorgos Vasdekis, Rui-Yang Zhang

CoRR, 2024

Measure transport with kernel mean embeddings.

Linfeng Wang, Nikolas Nüsken

CoRR, 2024

Transport meets Variational Inference: Controlled Monte Carlo Diffusions.

Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Bayesian learning via neural Schrödinger-Föllmer flows.

Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken

Stat. Comput., 2023

On the geometry of Stein variational gradient descent.

Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch

J. Mach. Learn. Res., 2023

Nonnegative matrix factorization for coherent set identification by direct low rank maximum likelihood estimation.

Robert Polzin, Ilja Klebanov, Nikolas Nüsken, Péter Koltai

CoRR, 2023

Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schrödinger Bridges.

Francisco Vargas, Nikolas Nüsken

CoRR, 2023

2021

Interpolating between BSDEs and PINNs - deep learning for elliptic and parabolic boundary value problems.

Nikolas Nüsken, Lorenz Richter

CoRR, 2021

Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering.

Michele Coghi, Torstein Nilssen, Nikolas Nüsken

CoRR, 2021

Stein Variational Gradient Descent: many-particle and long-time asymptotics.

Nikolas Nüsken, D. R. Michiel Renger

CoRR, 2021

Solving high-dimensional parabolic PDEs using the tensor train format.

Lorenz Richter, Leon Sallandt, Nikolas Nüsken

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Affine Invariant Interacting Langevin Dynamics for Bayesian Inference.

Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich

SIAM J. Appl. Dyn. Syst., 2020

Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space.

Nikolas Nüsken, Lorenz Richter

CoRR, 2020

VarGrad: A Low-Variance Gradient Estimator for Variational Inference.

Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Constructing Sampling Schemes via Coupling: Markov Semigroups and Optimal Transport.

Nikolas Nüsken, Grigorios A. Pavliotis

SIAM/ASA J. Uncertain. Quantification, 2019

State and Parameter Estimation from Observed Signal Increments.

Nikolas Nüsken, Sebastian Reich, Paul J. Rozdeba

Entropy, 2019

Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart.

Nikolas Nüsken, Sebastian Reich

CoRR, 2019