IDEAS home Printed from https://ideas.repec.org/r/crs/wpaper/2014-45.html

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

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Abdulrahman Alswaidan & Jeffrey D. Varner, 2026. "Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy," Papers 2603.06875, arXiv.org, revised May 2026.
  2. 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).
  3. 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).
  4. 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.
  5. Denis Belomestny & Leonid Iosipoi, 2019. "Fourier transform MCMC, heavy tailed distributions and geometric ergodicity," Papers 1909.00698, arXiv.org, revised Dec 2019.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Marelys Crespo & Sébastien Gadat & Xavier Gendre, 2024. "Stochastic gradient langevin dynamics for (weakly) log-concave posterior distributions," Post-Print hal-04943092, HAL.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. Lytras, Iosif & Sabanis, Sotirios, 2025. "Taming under isoperimetry," Stochastic Processes and their Applications, Elsevier, vol. 188(C).
  19. Vincent Lemaire & Gilles Pag`es & Christian Yeo, 2023. "Swing contract pricing: with and without Neural Networks," Papers 2306.03822, arXiv.org, revised Mar 2024.
  20. Bolte, Jérôme & Miclo, Laurent & Villeneuve, Stéphane, 2022. "Swarm gradient dynamics for global optimization: the mean-field limit case," TSE Working Papers 22-1302, Toulouse School of Economics (TSE).
  21. 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.
  22. 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.
  23. Maxime Egéa & Fabien Panloup, 2025. "Multilevel Langevin Pathwise Average for Gibbs Approximation," Mathematics of Operations Research, INFORMS, vol. 50(1), pages 573-605, February.
  24. 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.
  25. Yu, Lu & Dalalyan, Arnak, 2025. "Parallelized midpoint randomization for Langevin Monte Carlo," Stochastic Processes and their Applications, Elsevier, vol. 190(C).
  26. 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.
  27. 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.
  28. 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).
  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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.