Nikolas Nüsken

29 papers A* 4Journal 25

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Coherent Set Identification Via Direct Low Rank Maximum Likelihood Estimation. J. Nonlinear Sci. Robert M. Polzin, Ilja Klebanov, Nikolas Nüsken, Péter Koltai
2025	A*	`conf`	Conditioning Diffusions Using Malliavin Calculus. ICML Jakiw Pidstrigach, Elizabeth Louise Baker, Carles Domingo-Enrich, George Deligiannidis, Nikolas Nüsken
2025	J	`jnl`	Conditioning Diffusions Using Malliavin Calculus. CoRR Jakiw Pidstrigach, Elizabeth Louise Baker, Carles Domingo-Enrich, George Deligiannidis, Nikolas Nüsken
2025	J	`jnl`	Control Consistency Losses for Diffusion Bridges. CoRR Samuel Howard, Nikolas Nüsken, Jakiw Pidstrigach
2024	J	`jnl`	From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. J. Mach. Learn. Res. Lorenz Richter, Leon Sallandt, Nikolas Nüsken
2024	J	`jnl`	Measure transport with kernel mean embeddings. CoRR Linfeng Wang, Nikolas Nüsken
2024	J	`jnl`	Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm. CoRR Samuel Livingstone, Nikolas Nüsken, Giorgos Vasdekis, Rui-Yang Zhang
2024	J	`jnl`	Stein transport for Bayesian inference. CoRR Nikolas Nüsken
2024	A*	`conf`	Transport meets Variational Inference: Controlled Monte Carlo Diffusions. ICLR Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken
2023	J	`jnl`	Bayesian learning via neural Schrödinger-Föllmer flows. Stat. Comput. Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
2023	J	`jnl`	From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. CoRR Lorenz Richter, Leon Sallandt, Nikolas Nüsken
2023	J	`jnl`	Nonnegative matrix factorization for coherent set identification by direct low rank maximum likelihood estimation. CoRR Robert Polzin, Ilja Klebanov, Nikolas Nüsken, Péter Koltai
2023	J	`jnl`	On the geometry of Stein variational gradient descent. J. Mach. Learn. Res. Andrew B. Duncan, Nikolas Nüsken, Lukasz Szpruch
2023	J	`jnl`	Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schrödinger Bridges. CoRR Francisco Vargas, Nikolas Nüsken
2021	J	`jnl`	Bayesian Learning via Neural Schrödinger-Föllmer Flows. CoRR Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
2021	J	`jnl`	Interpolating between BSDEs and PINNs - deep learning for elliptic and parabolic boundary value problems. CoRR Nikolas Nüsken, Lorenz Richter
2021	J	`jnl`	Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering. CoRR Michele Coghi, Torstein Nilssen, Nikolas Nüsken
2021	A*	`conf`	Solving high-dimensional parabolic PDEs using the tensor train format. ICML Lorenz Richter, Leon Sallandt, Nikolas Nüsken
2021	J	`jnl`	Solving high-dimensional parabolic PDEs using the tensor train format. CoRR Lorenz Richter, Leon Sallandt, Nikolas Nüsken
2021	J	`jnl`	Stein Variational Gradient Descent: many-particle and long-time asymptotics. CoRR Nikolas Nüsken, D. R. Michiel Renger
2020	J	`jnl`	Affine Invariant Interacting Langevin Dynamics for Bayesian Inference. SIAM J. Appl. Dyn. Syst. Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich
2020	J	`jnl`	Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space. CoRR Nikolas Nüsken, Lorenz Richter
2020	A*	`conf`	VarGrad: A Low-Variance Gradient Estimator for Variational Inference. NeurIPS Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz
2020	J	`jnl`	VarGrad: A Low-Variance Gradient Estimator for Variational Inference. CoRR Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz
2019	J	`jnl`	Affine invariant interacting Langevin dynamics for Bayesian inference. CoRR Alfredo Garbuno-Inigo, Nikolas Nüsken, Sebastian Reich
2019	J	`jnl`	Constructing Sampling Schemes via Coupling: Markov Semigroups and Optimal Transport. SIAM/ASA J. Uncertain. Quantification Nikolas Nüsken, Grigorios A. Pavliotis
2019	J	`jnl`	Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. CoRR Nikolas Nüsken, Sebastian Reich
2019	J	`jnl`	On the geometry of Stein variational gradient descent. CoRR Andrew B. Duncan, Nikolas Nüsken, Lukasz Szpruch
2019	J	`jnl`	State and Parameter Estimation from Observed Signal Increments. Entropy Nikolas Nüsken, Sebastian Reich, Paul J. Rozdeba