mlsanityData to 7 Oct 2026

Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning

arXiv 1903.10328 ↗First posted 25 Mar 2019stat.ML cs.LGcited by 8

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Abstract (arXiv)

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) is a momentum version of stochastic gradient descent with properly injected Gaussian noise to find a global minimum. In this paper, non-asymptotic convergence analysis of SGHMC is given in the context of non-convex optimization, where subsampling techniques are used over an i.i.d dataset for gradient updates. Our results complement those of [RRT17] and improve on those of [GGZ18].

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Mar 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 3; 2025: 1; 2026: 1.

8 papers.

  1. 2023Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Casecited by 12
  2. 2024Kinetic Interacting Particle Langevin Monte Carlocited by 7
  3. 2021Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlocited by 4
  4. 2024Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theorycited by 4
  5. 2021Hamiltonian Monte Carlo with Asymmetrical Momentum Distributionscited by 1
  6. 2024Error estimates between SGD with momentum and underdamped Langevin diffusioncited by 1
  7. 2025Contractive kinetic Langevin samplers beyond global Lipschitz continuitycited by 1
  8. 2026When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimizecited by 0