2 citations · 4 across the 15 of their papers we have counts for
22 papers
Target-adapted Green-Bessel SVGD: uniform-in-time propagation of chaos and last-iterate consistency
Trevor Teolis, Maarten V. de Hoop
We prove uniform-in-time propagation of chaos and last-iterate consistency for a target-adapted Stein variational gradient descent (SVGD) flow on compact connected manifolds. The t…
Generic Recovery of Permittivity and Permeability in Anisotropic Maxwell Systems
Antonio Cocan, Maarten V. de Hoop, Joonas Ilmavirta +2
We study the inverse problem of recovering the constitutive tensors of a homogeneous anisotropic electromagnetic medium without magnetoelectric coupling (non-chiral) from its Fresn…
Riesz-Kernel Stein Variational Gradient Descent: Renormalized Entropy and Long-Time Particle Limits
Trevor Teolis, Maarten V. de Hoop
Stein variational gradient descent (SVGD) transports interacting particles toward a target distribution through deterministic kernelized dynamics. Singular Riesz kernels are attrac…
Function graph transformers universally approximate operators between function spaces
Takashi Furuya, David Mis, Ivan Dokmanić +2
We study the approximation of nonlinear operators between function spaces by transformers. Our approach is to lift functions to measures supported on their graphs and leverage a re…
Training Infinitely Deep and Wide Transformers
Raphaël Barboni, Maarten V. de Hoop, Takashi Furuya +1
Transformers have become the dominant architecture in modern machine learning, yet the theoretical understanding of their training dynamics remains limited. This paper develops a r…
Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures
Lorenzo Baldassari, Josselin Garnier, Knut Solna +1
Obtaining stable diffusion-based samplers in high- and infinite-dimensional settings is challenging because errors can accumulate across high-frequency coordinates and make the dyn…