mlsanityData to 7 Oct 2026

On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case

arXiv 1905.13142 ↗First posted 30 May 2019math.ST math.PR stat.MLcited by 14

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Abstract (arXiv)

We consider the problem of sampling from a target distribution, which is emph not necessarily logconcave, in the context of empirical risk minimization and stochastic optimization as presented in Raginsky et al. (2017). Non-asymptotic analysis results are established in the L1-Wasserstein distance for the behaviour of Stochastic Gradient Langevin Dynamics (SGLD) algorithms. We allow the estimation of gradients to be performed even in the presence of dependent data streams. Our convergence estimates are sharper and uniform in the number of iterations, in contrast to those in previous studies.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2019.

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Citing papers per year: 2021: 4; 2022: 5; 2023: 2; 2024: 0; 2025: 2; 2026: 1.

14 papers.

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  1. 2021Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networkscited by 11
  2. 2021Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation functioncited by 10
  3. 2021Convergence Analysis of Schr"odinger-F"ollmer Sampler without Convexitycited by 7
  4. 2022Constrained Langevin Algorithms with L-mixing External Random Variablescited by 6
  5. 2023Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithmscited by 4
  6. 2021Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentialscited by 3
  7. 2022Weak Convergence of Approximate reflection coupling and its Application to Non-convex Optimizationcited by 2
  8. 2022A Dynamical System View of Langevin-Based Non-Convex Samplingcited by 2
  9. 2025Regime-Switching Langevin Monte Carlo Algorithmscited by 2
  10. 2025Anchored Langevin Algorithmscited by 2
  11. 2023On the strong stability of ergodic iterationscited by 1
  12. 2022Uniform Generalization Bound on Time and Inverse Temperature for Gradient Descent Algorithm and its Application to Analysis of Simulated Annealingcited by 0
2 more
  1. 2022Asynchronous Bayesian Learning over a Networkcited by 0
  2. 2026Improved Analysis for Hessian-free High-resolution Monte Carlo Samplingcited by 0