Search

N. Nüsken

Bayesian Learning via Neural Schrödinger-Föllmer Flows
Interpolating between BSDEs and PINNs – deep learning for elliptic and parabolic boundary value problems
Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering
Stein variational gradient descent: many-particle and long-time asymptotics
Solving high-dimensional parabolic PDEs using the tensor train format
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space
Affine Invariant Interacting Langevin Dynamics for Bayesian Inference
On the geometry of Stein variational gradient descent
Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart
State and Parameter Estimation from Observed Signal Increments
Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo
Constructing sampling schemes via coupling: Markov semigroups and optimal transport
Topics in sampling schemes based on Markov processes
Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions

Published with Wowchemy — the free, open source website builder that empowers creators.