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On stochastic gradient Langevin dynamics with dependent data streams in
  the logconcave case

On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case

6 December 2018
M. Barkhagen
N. H. Chau
'. Moulines
Miklós Rásonyi
S. Sabanis
Ying Zhang
ArXivPDFHTML

Papers citing "On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case"

12 / 12 papers shown
Title
On diffusion-based generative models and their error bounds: The
  log-concave case with full convergence estimates
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates
Stefano Bruno
Ying Zhang
Dong-Young Lim
Ömer Deniz Akyildiz
Sotirios Sabanis
DiffM
43
4
0
22 Nov 2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity --
  the Strongly Convex Case
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case
Tim Johnston
Iosif Lytras
Sotirios Sabanis
43
8
0
19 Jan 2023
Parametric estimation of stochastic differential equations via online
  gradient descent
Parametric estimation of stochastic differential equations via online gradient descent
Shogo H. Nakakita
36
3
0
17 Oct 2022
Unbiased time-average estimators for Markov chains
Unbiased time-average estimators for Markov chains
N. Kahalé
41
3
0
20 Sep 2022
Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials
D. Nguyen
40
5
0
17 Dec 2021
Uniform minorization condition and convergence bounds for
  discretizations of kinetic Langevin dynamics
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics
Alain Durmus
Aurélien Enfroy
Eric Moulines
G. Stoltz
34
17
0
30 Jul 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity
Jian Huang
Yuling Jiao
Lican Kang
Xu Liao
Jin Liu
Yanyan Liu
40
27
0
21 Jun 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient
  adaptive algorithms for neural networks
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
Dong-Young Lim
Sotirios Sabanis
44
11
0
28 May 2021
Taming neural networks with TUSLA: Non-convex learning via adaptive
  stochastic gradient Langevin algorithms
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms
A. Lovas
Iosif Lytras
Miklós Rásonyi
Sotirios Sabanis
19
25
0
25 Jun 2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo
  under local conditions for nonconvex optimization
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization
Ömer Deniz Akyildiz
Sotirios Sabanis
51
17
0
13 Feb 2020
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under
  local conditions in nonconvex optimization
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization
Ying Zhang
Ömer Deniz Akyildiz
Theodoros Damoulas
Sotirios Sabanis
26
44
0
04 Oct 2019
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning
Huy N. Chau
M. Rásonyi
43
10
0
25 Mar 2019
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