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Nüsken, Nikolas
29  results:
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1

Bayesian learning via neural Schrödinger–Föllmer flows:

Vargas, Francisco ; Ovsianas, Andrius ; Fernandes, David...
Statistics and Computing.  33 (2022)  1 - p. , 2022
Link: https://doi.org/10.1007/..
 
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2

Solving high-dimensional Hamilton–Jacobi–Bellman PDEs using..:

Nüsken, Nikolas ; Richter, Lorenz
Partial Differential Equations and Applications.  2 (2021)  4 - p. , 2021
Link: https://doi.org/10.1007/..
 
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3

Affine Invariant Interacting Langevin Dynamics for Bayesian..:

Garbuno-Inigo, Alfredo ; Nüsken, Nikolas ; Reich, Sebastian
SIAM Journal on Applied Dynamical Systems.  19 (2020)  3 - p. 1633-1658 , 2020
Link: https://doi.org/10.1137/..
 
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4

State and Parameter Estimation from Observed Signal Increme..:

Nüsken, Nikolas ; Reich, Sebastian ; Rozdeba, Paul J.
Entropy.  21 (2019)  5 - p. 505 , 2019
Link: https://doi.org/10.3390/..
 
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5

Transport meets Variational Inference: Controlled Monte Car..:

Vargas, Francisco ; Padhy, Shreyas ; Blessing, Denis.
http://arxiv.org/abs/2307.01050.  , 2023
Link: http://arxiv.org/abs/230..
 
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6

Coherent set identification via direct low rank maximum lik..:

Polzin, Robert ; Klebanov, Ilja ; Nüsken, Nikolas.
http://arxiv.org/abs/2308.07663.  , 2023
Link: http://arxiv.org/abs/230..
 
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7

From continuous-time formulations to discretization schemes..:

Richter, Lorenz ; Sallandt, Leon ; Nüsken, Nikolas
http://arxiv.org/abs/2307.15496.  , 2023
Link: http://arxiv.org/abs/230..
 
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8

Hypocoercivity of Piecewise Deterministic Markov Process-Mo..:

Andrieu, Christophe ; Durmus, Alain ; Nüsken, Nikolas.
Andrieu , C , Durmus , A , Nüsken , N & Roussel , J 2021 , ' Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo ' , Annals of Applied Probability , vol. 31 , no. 5 , pp. 2478-2517 . https://doi.org/10.1214/20-AAP1653.  , 2021
Link: https://hdl.handle.net/1..
 
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9

Hypocoercivity of Piecewise Deterministic Markov Process-Mo..:

Andrieu, Christophe ; Durmus, Alain ; Nüsken, Nikolas.
info:eu-repo/semantics/altIdentifier/arxiv/1808.08592.  , 2021
Link: https://hal.science/hal-..
 
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10

Interpolating between BSDEs and PINNs: deep learning for el..:

Nüsken, Nikolas ; Richter, Lorenz
http://arxiv.org/abs/2112.03749.  , 2021
Link: http://arxiv.org/abs/211..
 
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11

Solving high-dimensional Hamilton–Jacobi–Bellman PDEs using..:

Nüsken, Nikolas ; Richter, Lorenz
https://refubium.fu-berlin.de/handle/fub188/33499.  , 2021
Link: https://refubium.fu-berl..
 
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12

Rough McKean-Vlasov dynamics for robust ensemble Kalman fil..:

Coghi, Michele ; Nilssen, Torstein ; Nüsken, Nikolas.
http://arxiv.org/abs/2107.06621.  , 2021
Link: http://arxiv.org/abs/210..
 
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13

Solving high-dimensional parabolic PDEs using the tensor tr..:

Richter, Lorenz ; Sallandt, Leon ; Nüsken, Nikolas
http://arxiv.org/abs/2102.11830.  , 2021
Link: http://arxiv.org/abs/210..
 
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14

Stein variational gradient descent: Many-particle and long-..:

Nüsken, Nikolas ; Renger, D. R. Michiel
ISSN:2198-5855.  , 2021
Link: https://oa.tib.eu/renate..
 
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15

Bayesian Learning via Neural Schr\"odinger-F\"ollmer Flows:

Vargas, Francisco ; Ovsianas, Andrius ; Fernandes, David...
http://arxiv.org/abs/2111.10510.  , 2021
Link: http://arxiv.org/abs/211..
 
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