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Dimension-free convergence rates for gradient Langevin dynamics in RKHS
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Dimension-free convergence rates for gradient Langevin dynamics in RKHS

Boris Muzellec
Kanji Sato
Mathurin Massias
Taiji Suzuki
ArXiv (abs)PDFHTML

Papers citing "Dimension-free convergence rates for gradient Langevin dynamics in RKHS"

2 / 2 papers shown
Title
Deep Stochastic Mechanics
Deep Stochastic Mechanics
Elena Orlova
Aleksei Ustimenko
Ruoxi Jiang
Peter Y. Lu
Rebecca Willett
DiffM
85
0
0
31 May 2023
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Taiji Suzuki
Shunta Akiyama
MLT
65
12
0
06 Dec 2020