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(Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
v1v2 (latest)

(Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics

2 January 2015
Sebastian J. Vollmer
K. Zygalakis
and Yee Whye Teh
ArXiv (abs)PDFHTML

Papers citing "(Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics"

25 / 25 papers shown
Title
From Global to Local: A Scalable Benchmark for Local Posterior Sampling
From Global to Local: A Scalable Benchmark for Local Posterior Sampling
Rohan Hitchcock
Jesse Hoogland
118
1
0
29 Jul 2025
Aggregated Gradient Langevin Dynamics
Aggregated Gradient Langevin DynamicsAAAI Conference on Artificial Intelligence (AAAI), 2019
Chao Zhang
Jiahao Xie
Zebang Shen
P. Zhao
Tengfei Zhou
Hui Qian
185
1
0
21 Oct 2019
Validated Variational Inference via Practical Posterior Error Bounds
Validated Variational Inference via Practical Posterior Error BoundsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Jonathan H. Huggins
Mikolaj Kasprzak
Trevor Campbell
Tamara Broderick
308
41
0
09 Oct 2019
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Cyclical Stochastic Gradient MCMC for Bayesian Deep LearningInternational Conference on Learning Representations (ICLR), 2019
Ruqi Zhang
Chunyuan Li
Jianyi Zhang
Changyou Chen
A. Wilson
BDL
227
290
0
11 Feb 2019
Practical bounds on the error of Bayesian posterior approximations: A
  nonasymptotic approach
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Jonathan H. Huggins
Trevor Campbell
Mikolaj Kasprzak
Tamara Broderick
189
31
0
25 Sep 2018
On sampling from a log-concave density using kinetic Langevin diffusions
On sampling from a log-concave density using kinetic Langevin diffusions
A. Dalalyan
L. Riou-Durand
379
166
0
24 Jul 2018
Scalable Natural Gradient Langevin Dynamics in Practice
Scalable Natural Gradient Langevin Dynamics in Practice
Henri Palacci
H. Hess
BDL
99
8
0
07 Jun 2018
Three Factors Influencing Minima in SGD
Three Factors Influencing Minima in SGD
Stanislaw Jastrzebski
Zachary Kenton
Devansh Arpit
Nicolas Ballas
Asja Fischer
Yoshua Bengio
Amos Storkey
327
502
0
13 Nov 2017
User-friendly guarantees for the Langevin Monte Carlo with inaccurate
  gradient
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
A. Dalalyan
Avetik G. Karagulyan
422
311
0
29 Sep 2017
Control Variates for Stochastic Gradient MCMC
Control Variates for Stochastic Gradient MCMC
Jack Baker
Paul Fearnhead
E. Fox
Christopher Nemeth
BDL
217
105
0
16 Jun 2017
Geometry and Dynamics for Markov Chain Monte Carlo
Geometry and Dynamics for Markov Chain Monte Carlo
Alessandro Barp
François‐Xavier Briol
A. Kennedy
Mark Girolami
AI4CE
161
31
0
08 May 2017
Differentially Private Neighborhood-based Recommender Systems
Differentially Private Neighborhood-based Recommender SystemsIFIP International Information Security Conference (IFIP SEC), 2017
Jun Wang
Qiang Tang
134
12
0
09 Jan 2017
On the Convergence of Stochastic Gradient MCMC Algorithms with
  High-Order Integrators
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
Changyou Chen
Nan Ding
Lawrence Carin
150
166
0
21 Oct 2016
Stochastic Gradient MCMC with Stale Gradients
Stochastic Gradient MCMC with Stale Gradients
Changyou Chen
Nan Ding
Chunyuan Li
Yizhe Zhang
Lawrence Carin
BDL
183
23
0
21 Oct 2016
Multilevel Monte Carlo for Scalable Bayesian Computations
Multilevel Monte Carlo for Scalable Bayesian Computations
M. Giles
Tigran Nagapetyan
Lukasz Szpruch
Sebastian J. Vollmer
K. Zygalakis
157
9
0
15 Sep 2016
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis
  of Big Data
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big DataAnnals of Statistics (Ann. Stat.), 2016
J. Bierkens
Paul Fearnhead
Gareth O. Roberts
226
240
0
11 Jul 2016
Quantifying the accuracy of approximate diffusions and Markov chains
Quantifying the accuracy of approximate diffusions and Markov chains
Jonathan H. Huggins
James Zou
323
29
0
20 May 2016
Multilevel Monte Carlo methods for the approximation of invariant
  measures of stochastic differential equations
Multilevel Monte Carlo methods for the approximation of invariant measures of stochastic differential equations
M. Giles
Mateusz B. Majka
Lukasz Szpruch
Sebastian J. Vollmer
K. Zygalakis
297
4
0
04 May 2016
Bridging the Gap between Stochastic Gradient MCMC and Stochastic
  Optimization
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Changyou Chen
David Carlson
Zhe Gan
Chunyuan Li
Lawrence Carin
195
92
0
25 Dec 2015
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale
  Bayesian Sampling
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
Xiaocheng Shang
Zhanxing Zhu
Benedict Leimkuhler
Amos J. Storkey
146
53
0
29 Oct 2015
Optimal approximating Markov chains for Bayesian inference
Optimal approximating Markov chains for Bayesian inference
J. Johndrow
Jonathan C. Mattingly
Sayan Mukherjee
David B. Dunson
246
31
0
13 Aug 2015
Provable Bayesian Inference via Particle Mirror Descent
Provable Bayesian Inference via Particle Mirror DescentInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2015
Bo Dai
Niao He
H. Dai
Le Song
371
76
0
09 Jun 2015
Fast Differentially Private Matrix Factorization
Fast Differentially Private Matrix Factorization
Ziqi Liu
Yu Wang
Alex Smola
FedML
241
130
0
06 May 2015
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu Wang
S. Fienberg
Alex Smola
226
250
0
26 Feb 2015
Enabling scalable stochastic gradient-based inference for Gaussian
  processes by employing the Unbiased LInear System SolvEr (ULISSE)
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Maurizio Filippone
Raphael Engler
305
32
0
22 Jan 2015
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