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Theoretical guarantees for approximate sampling from smooth and log-concave densities

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Cited by:

  1. Loaiza-Maya, Rubén & Nibbering, Didier & Zhu, Dan, 2024. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Journal of Econometrics, Elsevier, vol. 241(2).
  2. Crespo, Marelys & Gadat, Sébastien & Gendre, Xavier, 2023. "Stochastic Langevin Monte Carlo for (weakly) log-concave posterior distributions," TSE Working Papers 23-1398, Toulouse School of Economics (TSE).
  3. Denis Belomestny & Leonid Iosipoi, 2019. "Fourier transform MCMC, heavy tailed distributions and geometric ergodicity," Papers 1909.00698, arXiv.org, revised Dec 2019.
  4. Xuefeng Gao & Mert Gürbüzbalaban & Lingjiong Zhu, 2022. "Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Nonconvex Stochastic Optimization: Nonasymptotic Performance Bounds and Momentum-Based Acceleration," Operations Research, INFORMS, vol. 70(5), pages 2931-2947, September.
  5. Dong-Young Lim & Ariel Neufeld & Sotirios Sabanis & Ying Zhang, 2025. "Langevin Dynamics Based Algorithm e-TH ε O POULA for Stochastic Optimization Problems with Discontinuous Stochastic Gradient," Mathematics of Operations Research, INFORMS, vol. 50(3), pages 2333-2374, August.
  6. Marelys Crespo & Sébastien Gadat & Xavier Gendre, 2024. "Stochastic gradient langevin dynamics for (weakly) log-concave posterior distributions," Post-Print hal-04943092, HAL.
  7. M. Barkhagen & S. García & J. Gondzio & J. Kalcsics & J. Kroeske & S. Sabanis & A. Staal, 2023. "Optimising portfolio diversification and dimensionality," Journal of Global Optimization, Springer, vol. 85(1), pages 185-234, January.
  8. Lulu Zhang & Zhi-Qin John Xu & Yaoyu Zhang, 2022. "Data-informed deep optimization," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-21, June.
  9. Yang, Jun & Roberts, Gareth O. & Rosenthal, Jeffrey S., 2020. "Optimal scaling of random-walk metropolis algorithms on general target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(10), pages 6094-6132.
  10. Vincent Lemaire & Gilles Pag`es & Christian Yeo, 2023. "Swing contract pricing: with and without Neural Networks," Papers 2306.03822, arXiv.org, revised Mar 2024.
  11. Villeneuve, Stéphane & Bolte, Jérôme & Miclo, Laurent, 2022. "Swarm gradient dynamics for global optimization: the mean-field limit case," TSE Working Papers 22-1302, Toulouse School of Economics (TSE).
  12. Maxime Egéa & Fabien Panloup, 2025. "Multilevel Langevin Pathwise Average for Gibbs Approximation," Mathematics of Operations Research, INFORMS, vol. 50(1), pages 573-605, February.
  13. Tengyuan Liang & Weijie J. Su, 2019. "Statistical inference for the population landscape via moment‐adjusted stochastic gradients," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 431-456, April.
  14. Yu, Lu & Dalalyan, Arnak, 2025. "Parallelized midpoint randomization for Langevin Monte Carlo," Stochastic Processes and their Applications, Elsevier, vol. 190(C).
  15. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
  16. Menz, Georg & Schlichting, André & Tang, Wenpin & Wu, Tianqi, 2022. "Ergodicity of the infinite swapping algorithm at low temperature," Stochastic Processes and their Applications, Elsevier, vol. 151(C), pages 519-552.
  17. Bally, Vlad & Qin, Yifeng, 2024. "Approximation for the invariant measure with applications for jump processes (convergence in total variation distance)," Stochastic Processes and their Applications, Elsevier, vol. 176(C).
  18. Abdulrahman Alswaidan & Jeffrey D. Varner, 2026. "Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy," Papers 2603.06875, arXiv.org, revised May 2026.
  19. Tung Duy Luu & Jalal Fadili & Christophe Chesneau, 2021. "Sampling from Non-smooth Distributions Through Langevin Diffusion," Methodology and Computing in Applied Probability, Springer, vol. 23(4), pages 1173-1201, December.
  20. Gadat, Sébastien & Panloup, Fabien & Pellegrini, C., 2020. "On the cost of Bayesian posterior mean strategy for log-concave models," TSE Working Papers 20-1155, Toulouse School of Economics (TSE), revised Feb 2022.
  21. Brosse, Nicolas & Durmus, Alain & Moulines, Éric & Sabanis, Sotirios, 2019. "The tamed unadjusted Langevin algorithm," Stochastic Processes and their Applications, Elsevier, vol. 129(10), pages 3638-3663.
  22. Maulén S. Rodrigo & Jalal Fadili & Hedy Attouch, 2025. "An Stochastic Differential Equation Perspective on Stochastic Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 50(4), pages 3190-3221, November.
  23. Belomestny, Denis & Iosipoi, Leonid, 2021. "Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 351-363.
  24. Jiaming Liang & Yongxin Chen, 2026. "Proximal Oracles for Optimization and Sampling," Journal of Optimization Theory and Applications, Springer, vol. 208(3), pages 1-34, March.
  25. Matthias Schmal & Patrick Mäder, 2026. "Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms," Nature Communications, Nature, vol. 17(1), pages 1-12, December.
  26. Lytras, Iosif & Sabanis, Sotirios, 2025. "Taming under isoperimetry," Stochastic Processes and their Applications, Elsevier, vol. 188(C).
  27. Chau, Huy N. & Rásonyi, Miklós, 2022. "Stochastic Gradient Hamiltonian Monte Carlo for non-convex learning," Stochastic Processes and their Applications, Elsevier, vol. 149(C), pages 341-368.
  28. Samuel Livingstone & Giacomo Zanella, 2022. "The Barker proposal: Combining robustness and efficiency in gradient‐based MCMC," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 496-523, April.
  29. Arnak Dalalyan, 2017. "Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent," Working Papers 2017-21, Center for Research in Economics and Statistics.
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