Nikolas Nüsken

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 23

Top Topics

Variational Inference

Top Venues

SIAM/ASA J. Uncertain. Quantification

SIAM J. Appl. Dyn. Syst.

J. Mach. Learn. Res.

Publications

Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken
Transport meets Variational Inference: Controlled Monte Carlo Diffusions. ICLR (2024)
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 (2024)
Linfeng Wang, Nikolas Nüsken
Measure transport with kernel mean embeddings. CoRR (2024)
Francisco Vargas, Nikolas Nüsken
Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schrödinger Bridges. CoRR (2023)
Lorenz Richter, Leon Sallandt, Nikolas Nüsken
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. CoRR (2023)
Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. 33 (1) (2023)
Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch
On the geometry of Stein variational gradient descent. J. Mach. Learn. Res. 24 (2023)
Robert Polzin, Ilja Klebanov, Nikolas Nüsken, Péter Koltai
Nonnegative matrix factorization for coherent set identification by direct low rank maximum likelihood estimation. CoRR (2023)
Lorenz Richter, Leon Sallandt, Nikolas Nüsken
Solving high-dimensional parabolic PDEs using the tensor train format. ICML (2021)
Nikolas Nüsken, D. R. Michiel Renger
Stein Variational Gradient Descent: many-particle and long-time asymptotics. CoRR (2021)
Michele Coghi, Torstein Nilssen, Nikolas Nüsken
Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering. CoRR (2021)
Lorenz Richter, Leon Sallandt, Nikolas Nüsken
Solving high-dimensional parabolic PDEs using the tensor train format. CoRR (2021)
Nikolas Nüsken, Lorenz Richter
Interpolating between BSDEs and PINNs - deep learning for elliptic and parabolic boundary value problems. CoRR (2021)
Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR (2021)
Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich
Affine Invariant Interacting Langevin Dynamics for Bayesian Inference. SIAM J. Appl. Dyn. Syst. 19 (3) (2020)
Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz
VarGrad: A Low-Variance Gradient Estimator for Variational Inference. NeurIPS (2020)
Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz
VarGrad: A Low-Variance Gradient Estimator for Variational Inference. CoRR (2020)
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 (2020)
Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich
Affine invariant interacting Langevin dynamics for Bayesian inference. CoRR (2019)
Nikolas Nüsken, Sebastian Reich, Paul J. Rozdeba
State and Parameter Estimation from Observed Signal Increments. Entropy 21 (5) (2019)
Nikolas Nüsken, Sebastian Reich
Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. CoRR (2019)
A. Duncan, Nikolas Nüsken, Lukasz Szpruch
On the geometry of Stein variational gradient descent. CoRR (2019)
Nikolas Nüsken, Grigorios A. Pavliotis
Constructing Sampling Schemes via Coupling: Markov Semigroups and Optimal Transport. SIAM/ASA J. Uncertain. Quantification 7 (1) (2019)