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2023

Coghi, Michele and Nilssen, Torstein and Nüsken, Nikolas and Reich, Sebastian (2023) Rough McKean–Vlasov dynamics for robust ensemble Kalman filtering. The Annals of Applied Probability, 33 (6b). pp. 5693-5752.

Polzin, Robert and Klebanov, Ilja and Nüsken, Nikolas and Koltai, Péter (2023) Nonnegative matrix factorization for coherent set identification by direct low rank maximum likelihood estimation. arXiv . pp. 1-43. (Unpublished)

Richter, Lorenz and Sallandt, Leon and Nüsken, Nikolas (2023) From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. Preprint . (Unpublished)

Nüsken, Nikolas and Richter, Lorenz (2023) Interpolating Between BSDEs and PINNs: Deep Learning for Elliptic and Parabolic Boundary Value Problems. Journal of Machine Learning, 2 . pp. 31-64.

2021

Coghi, Michele and Nilssen, Torstein and Nüsken, Nikolas (2021) Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering. arXive . pp. 1-41. (Submitted)

Nüsken, Nikolas and Richter, Lorenz (2021) Solving high-dimensional Hamilton–Jacobi–Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space. Partial Differential Equations and Applications, 2 (48). pp. 1-48.

Nüsken, Nikolas and Renger, D.R. Michiel (2021) Stein Variational Gradient Descent:many-particle and long-time asymptotics. arxiv preprint . pp. 1-25. (Submitted)

Richter, Lorenz and Sallandt, Leon and Nüsken, Nikolas (2021) Solving high-dimensional parabolic PDEs using the tensor train format. Proceedings of the 38th International Conferenceon Machine Learning, 139 . pp. 8998-9009.

2020

Richter, Lorenz and Boustati, Ayman and Nüsken, Nikolas and Ruiz, Francisco J. R. and Akyildiz, Ömer Deniz (2020) VarGrad: A Low-Variance Gradient Estimator for Variational Inference. Advances in Neural Information Processing Systems 2020 . pp. 1-25. (Submitted)

Garbuno-Inigo, Alfredo and Nüsken, Nikolas and Reich, Sebastian (2020) Affine Invariant Interacting Langevin Dynamics for Bayesian Inference. SIAM J. APPLIED DYNAMICAL SYSTEMS, 19 (3). pp. 1633-1658.