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On stochastic gradient Langevin dynamics with dependent data streams:
  the fully non-convex case
v1v2v3v4 (latest)

On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case

30 May 2019
N. H. Chau
'. Moulines
Miklós Rásonyi
Sotirios Sabanis
Ying Zhang
ArXiv (abs)PDFHTML

Papers citing "On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case"

18 / 18 papers shown
Title
Asynchronous Bayesian Learning over a Network
Asynchronous Bayesian Learning over a Network
Kinjal Bhar
H. Bai
Jemin George
Carl E. Busart
FedML
56
0
0
16 Nov 2022
Constrained Langevin Algorithms with L-mixing External Random Variables
Constrained Langevin Algorithms with L-mixing External Random Variables
Yu Zheng
Andrew G. Lamperski
83
6
0
27 May 2022
Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials
D. Nguyen
58
5
0
17 Dec 2021
Non-asymptotic estimates for TUSLA algorithm for non-convex learning
  with applications to neural networks with ReLU activation function
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function
Dongjae Lim
Ariel Neufeld
Sotirios Sabanis
Ying Zhang
80
20
0
19 Jul 2021
Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without
  Convexity
Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without Convexity
Yuling Jiao
Lican Kang
Yanyan Liu
Youzhou Zhou
OT
53
6
0
10 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
98
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
97
12
0
28 May 2021
Projected Stochastic Gradient Langevin Algorithms for Constrained
  Sampling and Non-Convex Learning
Projected Stochastic Gradient Langevin Algorithms for Constrained Sampling and Non-Convex Learning
Andrew G. Lamperski
42
28
0
22 Dec 2020
Faster Convergence of Stochastic Gradient Langevin Dynamics for
  Non-Log-Concave Sampling
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
Difan Zou
Pan Xu
Quanquan Gu
108
36
0
19 Oct 2020
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
88
26
0
25 Jun 2020
Scalable Control Variates for Monte Carlo Methods via Stochastic
  Optimization
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Shijing Si
Chris J. Oates
Andrew B. Duncan
Lawrence Carin
F. Briol
BDL
58
21
0
12 Jun 2020
Overall error analysis for the training of deep neural networks via
  stochastic gradient descent with random initialisation
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation
Arnulf Jentzen
Timo Welti
65
17
0
03 Mar 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
97
17
0
13 Feb 2020
Wasserstein Control of Mirror Langevin Monte Carlo
Wasserstein Control of Mirror Langevin Monte Carlo
Kelvin Shuangjian Zhang
Gabriel Peyré
M. Fadili
Marcelo Pereyra
71
66
0
11 Feb 2020
Full error analysis for the training of deep neural networks
Full error analysis for the training of deep neural networks
C. Beck
Arnulf Jentzen
Benno Kuckuck
77
47
0
30 Sep 2019
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning
Huy N. Chau
M. Rásonyi
71
10
0
25 Mar 2019
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
M. Barkhagen
N. H. Chau
'. Moulines
Miklós Rásonyi
S. Sabanis
Ying Zhang
92
38
0
06 Dec 2018
Solving the Kolmogorov PDE by means of deep learning
Solving the Kolmogorov PDE by means of deep learning
C. Beck
S. Becker
Philipp Grohs
Nor Jaafari
Arnulf Jentzen
83
96
0
01 Jun 2018
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