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Found 8 papers, 4 papers with code
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs
The numerical approximation of partial differential equations (PDEs) poses formidable challenges in high dimensions since classical grid-based methods suffer from the so-called curse of dimensionality.
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space.
Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems
Solving high-dimensional partial differential equations is a recurrent challenge in economics, science and engineering.
Bayesian Learning via Neural Schrödinger-Föllmer Flows
In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i. e. Schr\"odinger bridges).
Stein Variational Gradient Descent: many-particle and long-time asymptotics
Stein variational gradient descent (SVGD) refers to a class of methods for Bayesian inference based on interacting particle systems.
Solving high-dimensional parabolic PDEs using the tensor train format
High-dimensional partial differential equations (PDEs) are ubiquitous in economics, science and engineering.
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
We analyse the properties of an unbiased gradient estimator of the ELBO for variational inference, based on the score function method with leave-one-out control variates.
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space
Optimal control of diffusion processes is intimately connected to the problem of solving certain Hamilton-Jacobi-Bellman equations.
