Langevin Dynamics

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11 papers in the last four weeks, up 120% on the four weeks before. 0.1% of all new papers.

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Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
May 29, 2026Xiaoyu Wang, Jonathan H. HugginsMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics & Statistics, Boston University · Faculty of Computing & Data Sciences, Boston University
Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
May 29, 2026Yu Wang, Jie Ding, Jonathan H. HugginsMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics & Statistics, Boston University · Questrom School of Business, Boston University · Faculty of Computing & Data Sciences, Boston University
Improved Guarantees for Langevin Monte Carlo with Average Smoothness
May 29, 2026Arnak S. Dalalyan, Avetik KaragulyanMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
CREST, ENSAE Paris, Institut Polytechnique de Paris and MBZUAI · L2S, CNRS, Centrale Supélec, Université Paris-Saclay
Thinned Mean Field Langevin Dynamics
May 27, 2026Zonghao Chen, Heishiro Kanagawa, François-Xavier Briol +2Langevin Dynamics Mean-Field Theory
University College London · 2Newcastle University · 3The Alan Turing Institute +1
On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy
May 22, 2026Aratrika Mustafi, Soumya MukherjeeLangevin Dynamics Differential Privacy
Department of Statistics, Pennsylvania State University
Theoretical guidelines for annealed Langevin dynamics in compositional simulation-based inference
May 20, 2026Camille Touron, Gabriel V. Cardoso, Julyan Arbel +1Simulation-Based Inference Langevin Dynamics
Univ. Grenoble Alpes, Inria CNRS, Grenoble INP, LJK, France · Geostatistics team, Centre for geosciences and geoengineering Mines Paris, PSL University, Fontaineableau, France
Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics
May 18, 2026Benjamin Sterling, Mónica F. Bugallo, Tom TirerLangevin Dynamics Memorization in Generative Models
Department of Applied Math & Statistics Stony Brook University Stony Brook, NY 11790 · Department of Electrical and Computer Engineering Stony Brook University Stony Brook, NY 11790 · Faculty of Engineering Bar-Ilan University Ramat-Gan 5290002, Israel
Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures
May 15, 2026Lorenzo Baldassari, Josselin Garnier, Knut Solna +1Langevin Dynamics Diffusion Sampling
University of Basel · Ecole Polytechnique, IP Paris · University of California Irvine +1
Training-Free Generative Sampling via Moment-Matched Score Smoothing
May 14, 2026Zhenyu Yao, Daniel PaulinLangevin Dynamics Diffusion Sampling
College of Computing and Data Science Nanyang Technological University
Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
May 8, 2026Atsushi Nitanda, Dake Bu, Yueming Lyu +1Diffusion Model Guidance Langevin Dynamics
Agency for Science, Technology and Research (A⋆STAR) · Nanyang Technological University · City University of Hong Kong
Conservative Flows: A New Paradigm of Generative Models
May 7, 2026Eshed Gal, Md Shahriar Rahim Siddiqui, Moshe Eliasof +1Langevin Dynamics Generative Flow Networks
The University of British Columbia Vancouver, Canada · University of Cambridge Cambridge, United Kingdom
Time-Inhomogeneous Preconditioned Langevin Dynamics
May 7, 2026Alexander Falk, Laurenz Nagler, Andreas Habring +1Markov Chain Monte Carlo Langevin Dynamics
Institute of Visual Computing Graz University of Technology Inffeldgasse 16, 8010 Graz
Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees
May 6, 2026Ahmad Aghapour, Erhan Bayraktar, Asaf CohenDiffusion Models for Image Restoration Langevin Dynamics
Department of Mathematics University of Michigan Ann Arbor, MI 48109, USA
Decentralized Proximal Stochastic Gradient Langevin Dynamics
May 1, 2026Mohammad Rafiqul Islam, Lingjiong ZhuMarkov Chain Monte Carlo Stochastic Gradient Langevin Dynamics
Department of Mathematics, Florida State University, Tallahassee, Florida, United States of America
Conditional Score-Based Modeling of Effective Langevin Dynamics
Apr 27, 2026Ludovico T. GiorginiLangevin Dynamics Model Calibration
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
Apr 20, 2026Kijung Jeon, Michael Muehlebach, Molei TaoConstrained Generative Modeling Langevin Dynamics
Georgia Institute of Technology · Max Planck Institute for Int. Sys.
Bias mitigation in graph diffusion models
Apr 2, 2026Meng Yu, Kun ZhanDiffusion Models Graph Diffusion
School of Information Science & Engineering, Lanzhou University
Generative Shape Reconstruction with Geometry-Guided Langevin Dynamics
Mar 27, 2026Linus Härenstam-Nielsen, Dmitrii Pozdeev, Thomas Dagès +23D Reconstruction 3D Shape Completion
Technical University of Munich · Munich Center for Machine Learning
On the Robustness of Langevin Dynamics to Score Function Error
Mar 11, 2026Daniel Yiming Cao, August Y. Chen, Karthik Sridharan +1Langevin Dynamics Score-Based Generative Modeling
Cornell University, Ithaca, USA
Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy
Mar 6, 2026Abdulrahman Alswaidan, Jeffrey D. VarnerHopfield Networks Energy-Based Models
R.F. Smith School of Chemical and Biomolecular Engineering Cornell University, Ithaca, NY 14850
Improved high-dimensional estimation with Langevin dynamics and stochastic weight averaging
Mar 6, 2026Stanley Wei, Alex Damian, Jason D. LeeLangevin Dynamics Single-Index Models
Princeton University · Harvard University · University of California, Berkeley
Riemannian Langevin Dynamics: Strong Convergence of Geometric Euler-Maruyama Scheme
Mar 4, 2026Zhiyuan Zhan, Masashi SugiyamaStochastic Differential Equations Langevin Dynamics
The University of Tokyo, Japan · RIKEN Center for AIP, Japan
Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence
Mar 2, 2026Shiyuan Zhang, Qiwei Di, Xuheng Li +1Markov Chain Monte Carlo Langevin Dynamics
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
Leakage and Second-Order Dynamics Improve Hippocampal RNN Replay
Feb 20, 2026Josue Casco-Rodriguez, Nanda H. Krishna, Richard G. BaraniukRecurrent Neural Networks Langevin Dynamics
Department of Electrical and Computer Engineering, Rice University, USA · Mila - Quebec AI Institute & Université de Montréal, Canada
RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics
Oct 14, 2025Danqi Liao, Chen Liu, Xingzhi Sun +8Molecular Generation Molecular Optimization
Yale University
CoCre-Sam (Kokkuri-san): Modeling Ouija Board as Collective Langevin Dynamics Sampling from Fused Language Models
Jul 16, 2025Tadahiro Taniguchi, Masatoshi Nagano, Haruumi Omoto +1Emergent Communication Large Language Model-Based Social Simulation
Graduate School of Informatics, Kyoto University, Japan · Ritsumeikan University, Japan · University of Reading, UK

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Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo

Xiaoyu Wang, Jonathan H. Huggins

Department of Mathematics & Statistics, Boston University · Faculty of Computing & Data Sciences, Boston University

Markov Chain Monte Carlo Stochastic Gradient Langevin Dynamics Latent Variable Models Uncertainty Quantification Langevin Dynamics Bayesian Inference

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Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. However, it remains unclear how to tune the algorithm's hyperparameters in a principled manner to ensure the uncertainty estimates are statistically meaningful. In this work, we address this gap in tuning guidance by developing a statistical scaling limit theory for SGLD--Gibbs. We derive a joint asymptotic limit for the global parameters and latent variables under appropriate space-time rescaling. We show that global parameters converge to a diffusion-type limit, while each latent variable converges to a jump process, reflecting the use of intermittent Gibbs updates. This joint jump-diffusion structure reveals how latent-variable randomness contributes to the stationary distribution of the global parameters. We leverage our results to propose explicit guidance on hyperparameter tuning for SGLD--Gibbs that ensures meaningful uncertainty quantification. Numerical experiments show that SGLD--Gibbs with our tuning guidance leads to better parameter estimates, uncertainty quantification, and predictive performance than stochastic variational inference.