Decentralized stochastic gradient Langevin dynamics and Hamiltonian Monte Carlo

Mert Gürbüzbalaban; Xuefeng Gao; Yuanhan Hu; Lingjiong Zhu

Publications · Journal article · J. Mach. Learn. Res. 2021

Decentralized stochastic gradient Langevin dynamics and Hamiltonian Monte Carlo

Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, Lingjiong Zhu

Journal of Machine Learning Research, 22(239), pp. 1–69, 2021.

arXiv:2007.00590 Code

In brief

Introduces decentralized versions of stochastic gradient Langevin dynamics and Hamiltonian Monte Carlo for Bayesian learning over networks of agents, with non-asymptotic guarantees on sampling accuracy.

Topics

Sampling & Langevin algorithms Distributed & decentralized learning

Cite

@article{gurbuzbalaban2021decentralizedlangevin,
  title   = {Decentralized stochastic gradient Langevin dynamics and Hamiltonian Monte Carlo},
  author  = {Mert Gürbüzbalaban and Xuefeng Gao and Yuanhan Hu and Lingjiong Zhu},
  year    = {2021},
  journal = {Journal of Machine Learning Research},
  volume  = {22(239)},
  pages   = {1--69},
  note    = {arXiv:2007.00590},
}

Relatedsame topics

Anchored Langevin algorithms (arXiv, 2025)
DIGing-SGLD: decentralized and scalable Langevin sampling over time-varying networks (arXiv, 2025)
High-order Langevin Monte Carlo algorithms (arXiv, 2025)
Generalized EXTRA stochastic gradient Langevin dynamics (arXiv, 2024)
Penalized overdamped and underdamped Langevin Monte Carlo algorithms for constrained sampling (J. Mach. Learn. Res., 2024)

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