Fetching the paper…
Reading the bibliography…
Stochastic gradient Langevin dynamics (SGLD) and stochastic gradient Hamiltonian Monte Carlo (SGHMC) are two popular Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference that can scale to large datasets, allowing to sample from the posterior distribution of the parameters of a statistical model given the input data and the prior distribution over the model parameters.
B. Can, S. Soori, N. S. Aybat, M. Dehvani, and M. Gürbüzbalaban · 1907
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate o ( 1 / k 2 ) o(1/k^{2})
Y. E. Nesterov · 1983
Earlier work this paper cites.
A class of Wasserstein metrics for probability distributions
C. R. Givens and R. M. Shortt · 1984
Earlier work this paper cites.
Problems in decentralized decision making and computation
J. N. Tsitsiklis · 1984
Earlier work this paper cites.
Introduction to Optimization. Translations Series in Mathematics and Engineering
B. T. Polyak · 1987
Earlier work this paper cites.
Parallel and Distributed Computation: Numerical Methods , volume 23
D. P. Bertsekas and J. N. Tsitsiklis · 1989
Earlier work this paper cites.
Spectral Graph Theory , volume 92
F. R. Chung and F. C. Graham · 1997
Earlier work this paper cites.
Online model selection based on the variational Bayes
M.-A. Sato · 2001
Earlier work this paper cites.
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
B. Swenson, S. Kar, H. V. Poor, J. M. F. Moura, and A. Jaech · 2003
Earlier work this paper cites.
Distributed Stochastic Gradient Descent and Convergence to Local Minima
B. Swenson, R. Murray, S. Kar, and H. V. Poor · 2003
Earlier work this paper cites.
Distributed maximum likelihood estimation for sensor networks
D. Blatt and A. Hero · 2004
Earlier work this paper cites.
Decentralized source localization and tracking wireless sensor networks
M. G. Rabbat and R. D. Nowak · 2004
Earlier work this paper cites.
Randomized gossip algorithms
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah · 2006
Earlier work this paper cites.
A space-time diffusion scheme for peer-to-peer least-squares estimation
L. Xiao, S. Boyd, and S. Lall · 2006
Earlier work this paper cites.
A First Course in Bayesian Statistical Methods , volume 580
P. D. Hoff · 2009
Earlier work this paper cites.
Distributed subgradient methods for multi-agent optimization
A. Nedic and A. Ozdaglar · 2009
Earlier work this paper cites.
Optimal Transport: Old and New
C. Villani · 2009
Earlier work this paper cites.
Online learning for latent Dirichlet allocation
M. Hoffman, F. R. Bach, and D. M. Blei · 2010
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
M. Welling and Y. W. Teh · 2011
Earlier work this paper cites.
Streaming variational Bayes
T. Broderick, N. Boyd, A. Wibisono, A. C. Wilson, and M. I. Jordan · 2013
Earlier work this paper cites.
Online learning of nonparametric mixture models via sequential variational approximation
D. Lin · 2013
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course , volume 87
Y. Nesterov · 2013
Earlier work this paper cites.
Parallelizing MCMC via Weierstrass sampler
X. Wang and D. B. Dunson · 2013
Earlier work this paper cites.
Distributed stochastic gradient MCMC
S. Ahn, B. Shahbaba, and M. Welling · 2014
Earlier work this paper cites.
Approximate decentralized Bayesian inference
T. Campbell and J. P. How · 2014
Earlier work this paper cites.
Stochastic gradient Hamiltonian Monte Carlo
T. Chen, E. Fox, and C. Guestrin · 2014
Earlier work this paper cites.
First-order methods of smooth convex optimization with inexact oracle
O. Devolder, F. Glineur, and Y. Nesterov · 2014
Earlier work this paper cites.
Asymptotically exact, embarrassingly parallel MCMC
W. Neiswanger, C. Wang, and E. P. Xing · 2014
Earlier work this paper cites.
Parallel MCMC with generalized elliptical slice sampling
R. Nishihara, I. Murray, and R. P. Adams · 2014
Earlier work this paper cites.
Stochastic Processes and Applications: Diffusion processes, the Fokker-Planck and Langevin Equations , volume 60
G. A. Pavliotis · 2014
Earlier work this paper cites.
Distributed Bayesian posterior sampling via moment sharing
M. Xu, B. Lakshminarayanan, Y. W. Teh, J. Zhu, and B. Zhang · 2014
Earlier work this paper cites.
Large-scale distributed Bayesian matrix factorization using stochastic gradient MCMC
S. Ahn, A. Korattikara, N. Liu, S. Rajan, and M. Welling · 2015
Earlier work this paper cites.
Sampling from a log-concave distribution with Projected Langevin Monte Carlo
S. Bubeck, R. Eldan, and J. Lehec · 2015
Earlier work this paper cites.
On the convergence of stochastic gradient MCMC algorithms with high-order integrators
C. Chen, N. Ding, and L. Carin · 2015
Earlier work this paper cites.
From averaging to acceleration, there is only a step-size
N. Flammarion and F. Bach · 2015
Cited alongside, same era.
Distributed inference for Dirichlet process mixture models
H. Ge, Y. Chen, M. Wan, and Z. Ghahramani · 2015
Cited alongside, same era.
Global convergence of the Heavy-ball method for convex optimization
E. Ghadimi, H. R. Feyzmahdavian, and M. Johansson · 2015
Cited alongside, same era.
Variational consensus Monte Carlo
M. Rabinovich, E. Angelino, and M. I. Jordan · 2015
Cited alongside, same era.
Parallelizing MCMC with random partition trees
X. Wang, F. Guo, K. A. Heller, and D. B. Dunson · 2015
Cited alongside, same era.
Variance reduction in stochastic gradient Langevin dynamics
K. A. Dubey, S. J. Reddi, S. A. Williamson, B. Poczos, A. J. Smola, and E. P. Xing · 2016
Cited alongside, same era.
On explicit L 2 L^{2} -convergence rate estimate for underdamped Langevin dynamics
Y. Cao, J. Lu, and L. Wang · 2019
Later among the works it cites.
On stochastic graident Langevin dynamics with dependent data streams: the fully non-convex case
N. H. Chau, E. Moulines, M. Rásonyi, S. Sabanis, and Y. Zhang · 2019
Later among the works it cites.
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
A. S. Dalalyan and A. G. Karagulyan · 2019
Later among the works it cites.
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
A. Durmus and E. Moulines · 2019
Later among the works it cites.
Couplings and quantitative contraction rates for Langevin dynamics
A. Eberle, A. Guillin, and R. Zimmer · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Coresets for scalable Bayesian logistic regression
J. Huggins, T. Campbell, and T. Broderick · 2016
Cited alongside, same era.
The computation of averages from equilibrium and nonequilibrium Langevin molecular dynamics
B. Leimkuhler, C. Matthews, and G. Stoltz · 2016
Cited alongside, same era.
Accelerated distributed Nesterov gradient descent for smooth and strongly convex functions
G. Qu and N. Li · 2016
Cited alongside, same era.
Bayes and big data: The consensus Monte Carlo algorithm
S. L. Scott, A. W. Blocker, F. V. Bonassi, H. A. Chipman, E. I. George, and R. E. McCulloch · 2016
Cited alongside, same era.
A differential equation for modeling Nesterov’s accelerated gradient method: Theory and insights
W. Su, S. Boyd, and E. J. Candes · 2016
Cited alongside, same era.
Consistency and fluctuations for stochastic gradient Langevin dynamics
Y. W. Teh, A. H. Thiery, and S. J. Vollmer · 2016
Cited alongside, same era.
A. Fallah, M. Gürbüzbalaban, A. Ozdaglar, U. Şimşekli, and L. Zhu · 2019
Later among the works it cites.
Optimal decentralized distributed algorithms for stochastic convex optimization
E. Gorbunov, D. Dvinskikh, and A. Gasnikov · 2019
Later among the works it cites.
An accelerated decentralized stochastic proximal algorithm for finite sums
H. Hendrikx, F. Bach, and L. Massoulié · 2019
Later among the works it cites.
Decentralized Bayesian learning over graphs
A. Lalitha, X. Wang, O. Kilinc, Y. Lu, T. Javidi, and F. Koushanfar · 2019
Later among the works it cites.
Optimal convergence rates for convex distributed optimization in networks
K. Scaman, F. Bach, S. Bubeck, Y. Lee, and L. Massoulié · 2019
Later among the works it cites.
A survey of distributed optimization
T. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K. H. Johansson · 2019
Later among the works it cites.
Y. Zhang, O. D. Akyildiz, T. Damoulas, and S. Sabanis · 2019
Later among the works it cites.
Stochastic gradient Hamiltonian Monte Carlo methods with recursive variance reduction
D. Zou, P. Xu, and Q. Gu · 2019
Later among the works it cites.
O. D. Akyildiz and S. Sabanis · 2020
Closest in time.
IDEAL: Inexact DEcentralized accelerated augmented Lagrangian method
Y. Arjevani, J. Bruna, B. Can, M. Gürbüzbalaban, S. Jegelka, and H. Lin · 2020
Closest in time.
Robust accelerated gradient methods for smooth strongly convex functions
N. S. Aybat, A. Fallah, M. Gürbüzbalaban, and A. Ozdaglar · 2020
Closest in time.
On sampling from a log-concave density using kinetic Langevin diffusions
A. S. Dalalyan and L. Riou-Durand · 2020
Closest in time.
Convergence analysis of Langevin Monte Carlo in chi-square divergence
M. A. Erdogdu and R. Hosseinzadeh · 2020
Closest in time.
Breaking reversibility accelerates Langevin dynamics for global non-convex optimization
X. Gao, M. Gürbüzbalaban, and L. Zhu · 2020
Closest in time.
Stochastic gradient Langevin dynamics on a distributed network
V. Kungurtsev · 2020
Closest in time.
Differentially Private Accelerated Optimization Algorithms
N. Kuru, Ş. İlker Birbil, M. Gürbüzbalaban, and S. Yildirim · 2020
Closest in time.
An improved analysis of stochastic gradient descent with momentum
Y. Liu, Y. Gao, and W. Yin · 2020
Closest in time.
Distributed gradient methods for convex machine learning problems in networks: Distributed optimization
A. Nedic · 2020
Closest in time.
Decentralized Langevin dynamics for Bayesian learning
A. Parayil, H. Bai, J. George, and P. Gurram · 2020
Closest in time.
Asymptotic network independence in distributed stochastic optimization for machine learning: Examining distributed and centralized stochastic gradient descent
S. Pu, A. Olshevsky, and I. C. Paschalidis · 2020
Closest in time.
Global consensus Monte Carlo
L. J. Rendell, A. M. Johansen, A. Lee, and N. Whiteley · 2020
Closest in time.
On distributed stochastic gradient algorithms for global optimization
B. Swenson, A. Sridhar, and H. V. Poor · 2020
Closest in time.
Distributed heavy-ball: A generalization and acceleration of first-order methods with gradient tracking
R. Xin and U. A. Khan · 2020
Closest in time.
Accelerated primal-dual algorithms for distributed smooth convex optimization over networks
J. Xu, Y. Tian, Y. Sun, and G. Scutari · 2020
Closest in time.
On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case
M. Barkhagen, N. Chau, E. Moulines, M. Rásonyi, S. Sabanis, and Y. Zhang · 2021
Closest in time.
Stochastic gradient-based distributed Bayesian estimation in cooperative sensor networks
J. Cadena, P. Ray, H. Chen, B. Soper, D. Rajan, A. Yen, and R. Goldhahn · 2021
Closest in time.
Is there an analog of Nesterov acceleration for gradient-based MCMC?
Y.-A. Ma, N. S. Chatterji, X. Cheng, N. Flammarion, P. L. Bartlett, and M. I. Jordan · 2021
Closest in time.
Robustness of accelerated first-order algorithms for strongly convex optimization problems
H. Mohammadi, M. Razaviyayn, and M. R. Jovanović · 2021
Closest in time.