Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. - Nikolas Nüsken; Sebastian Reich (CoRR, 2019)

State and Parameter Estimation from Observed Signal Increments. - Nikolas Nüsken; Sebastian Reich; Paul J. Rozdeba (Entropy, 2019)

Constructing Sampling Schemes via Coupling: Markov Semigroups and Optimal Transport. - Nikolas Nüsken; Grigorios A. Pavliotis (SIAM/ASA J. Uncertain. Quantification, 2019)

VarGrad: A Low-Variance Gradient Estimator for Variational Inference. - Lorenz Richter; Ayman Boustati; Nikolas Nüsken; Francisco J. R. Ruiz; Ömer Deniz Akyildiz (NeurIPS, 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)

Affine Invariant Interacting Langevin Dynamics for Bayesian Inference. - Alfredo Garbuno-Inigo; Nikolas Nüsken; Sebastian Reich (SIAM J. Appl. Dyn. Syst., 2020)

Solving high-dimensional parabolic PDEs using the tensor train format. - Lorenz Richter; Leon Sallandt; Nikolas Nüsken (ICML, 2021)

Stein Variational Gradient Descent: many-particle and long-time asymptotics. - Nikolas Nüsken; D. R. Michiel Renger (CoRR, 2021)

Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering. - Michele Coghi; Torstein Nilssen; Nikolas Nüsken (CoRR, 2021)

Interpolating between BSDEs and PINNs - deep learning for elliptic and parabolic boundary value problems. - Nikolas Nüsken; Lorenz Richter (CoRR, 2021)

Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schrödinger Bridges. - Francisco Vargas 001; Nikolas Nüsken (CoRR, 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)

On the geometry of Stein variational gradient descent. - Andrew B. Duncan; Nikolas Nüsken; Lukasz Szpruch (J. Mach. Learn. Res., 2023)

Bayesian learning via neural Schrödinger-Föllmer flows. - Francisco Vargas 001; Andrius Ovsianas; David Fernandes; Mark Girolami; Neil D. Lawrence; Nikolas Nüsken (Stat. Comput., 2023)

Transport meets Variational Inference: Controlled Monte Carlo Diffusions. - Francisco Vargas 001; Shreyas Padhy; Denis Blessing; Nikolas Nüsken (ICLR, 2024)

Measure transport with kernel mean embeddings. - Linfeng Wang; 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)

Stein transport for Bayesian inference. - Nikolas Nüsken (CoRR, 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)

Coherent Set Identification Via Direct Low Rank Maximum Likelihood Estimation. - Robert M. Polzin; Ilja Klebanov; Nikolas Nüsken; Péter Koltai (J. Nonlinear Sci., 2025)

Conditioning Diffusions Using Malliavin Calculus. - Jakiw Pidstrigach; Elizabeth Baker; Carles Domingo-Enrich; George Deligiannidis; Nikolas Nüsken (CoRR, 2025)