Publications

F. Vargas, A. Ovsianas, D. Fernandes, M. Girolami, N. D. Lawrence, N. Nüsken (2021). Bayesian Learning via Neural Schrödinger-Föllmer Flows. (submitted).

arxiv

N. Nüsken, L. Richter (2021). Interpolating between BSDEs and PINNs – deep learning for elliptic and parabolic boundary value problems. (submitted).

arxiv

M. Coghi, T. Nilssen, N. Nüsken, S. Reich (2021). Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering. (submitted).

arxiv

N. Nüsken, D.R.M. Renger (2021). Stein variational gradient descent: many-particle and long-time asymptotics. (submitted).

arxiv

L. Richter, L. Sallandt, N. Nüsken (2021). Solving high-dimensional parabolic PDEs using the tensor train format. (submitted).

arxiv

L. Richter, A. Boustati, N. Nüsken, A. Akyildiz, F.J.R. Ruiz (2020). VarGrad: A Low-Variance Gradient Estimator for Variational Inference. In NeurIPS.

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N. Nüsken, L. Richter (2020). Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space. (submitted).

arxiv

A. Garbuno-Inigo, N. Nüsken, S. Reich (2020). Affine Invariant Interacting Langevin Dynamics for Bayesian Inference. In SIAM J. Appl. Dyn. Syst..

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A. Duncan, N. Nüsken, L. Szpruch (2019). On the geometry of Stein variational gradient descent. (submitted).

arxiv

N. Nüsken, S. Reich (2019). Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. (unpublished).

arxiv

N. Nüsken, S. Reich, P. Rozdeba (2019). State and Parameter Estimation from Observed Signal Increments. In Entropy.

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C. Andrieu, A. Durmus, N. Nüsken, J. Roussel (2019). Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo. (submitted).

arxiv

N. Nüsken, G.A. Pavliotis (2019). Constructing sampling schemes via coupling: Markov semigroups and optimal transport. In SIAM UQ.

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N. Nüsken (2018). Topics in sampling schemes based on Markov processes. PhD thesis.

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A. Duncan, N. Nüsken, G.A. Pavliotis (2017). Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions. In J. Stat. Phys..

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