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

Miklós Rásonyi

Sotirios Sabanis

ArXiv (abs)PDF HTML

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 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 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 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 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 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 Dong-Young Lim Sotirios Sabanis 97 12 0 28 May 2021
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 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 A. Lovas Iosif Lytras Miklós Rásonyi Sotirios Sabanis 88 26 0 25 Jun 2020
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 Arnulf Jentzen Timo Welti 65 17 0 03 Mar 2020
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 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 C. Beck Arnulf Jentzen Benno Kuckuck 77 47 0 30 Sep 2019
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 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 C. Beck S. Becker Philipp Grohs Nor Jaafari Arnulf Jentzen 83 96 0 01 Jun 2018