The promises and pitfalls of Stochastic Gradient Langevin Dynamics

25 November 2018

Papers citing "The promises and pitfalls of Stochastic Gradient Langevin Dynamics"

50 / 60 papers shown

Title
Laplace Sample Information: Data Informativeness Through a Bayesian Lens Johannes Kaiser Kristian Schwethelm Daniel Rueckert Georgios Kaissis 52 0 0 21 May 2025
Learning Image Fractals Using Chaotic Differentiable Point Splatting Adarsh Djeacoumar Felix Mujkanovic Hans-Peter Seidel Thomas Leimkuhler 84 1 0 24 Feb 2025
Random Reshuffling for Stochastic Gradient Langevin Dynamics Luke Shaw Peter A. Whalley 175 3 0 28 Jan 2025
Diffusion Model from Scratch Wang Zhen Dong Yunyun DiffM 106 0 0 14 Dec 2024
PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis Chuanhao Sun Thanos Triantafyllou Anthos Makris Maja Drmač Kai Xu Luo Mai Mahesh K. Marina 72 0 0 07 Oct 2024
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning S. Samsonov Eric Moulines Qi-Man Shao Zhuo-Song Zhang Alexey Naumov 97 5 0 26 May 2024
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics Lorenzo Mauri Giacomo Zanella 81 3 0 14 May 2024
3D Gaussian Splatting as Markov Chain Monte Carlo Shakiba Kheradmand Daniel Rebain Gopal Sharma Weiwei Sun Jeff Tseng Hossam N. Isack Abhishek Kar Andrea Tagliasacchi Kwang Moo Yi 3DGS 106 62 0 15 Apr 2024
Scalable Bayesian inference for the generalized linear mixed model S. Berchuck Felipe A. Medeiros Sayan Mukherjee Andrea Agazzi 65 0 0 05 Mar 2024
Accelerating Neural Field Training via Soft Mining Shakiba Kheradmand Daniel Rebain Gopal Sharma Hossam N. Isack Abhishek Kar Andrea Tagliasacchi Kwang Moo Yi 81 4 0 29 Nov 2023
An Empirical Bayes Framework for Open-Domain Dialogue Generation Jing Yang Lee Kong Aik Lee Woon-Seng Gan BDL 57 1 0 18 Nov 2023
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification Qianli Shen Wai Hoh Tang Zhun Deng Apostolos F. Psaros Kenji Kawaguchi 211 1 0 10 Oct 2023
Langevin Quasi-Monte Carlo Sifan Liu BDL 53 4 0 22 Sep 2023
Stochastic Gradient Langevin Dynamics Based on Quantization with Increasing Resolution Jinwuk Seok Chang-Jae Cho 50 0 0 30 May 2023
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms Tim Tsz-Kit Lau Han Liu Thomas Pock 97 4 0 25 May 2023
Machine Learning and the Future of Bayesian Computation Steven Winter Trevor Campbell Lizhen Lin Sanvesh Srivastava David B. Dunson TPM 77 4 0 21 Apr 2023
Piecewise Deterministic Markov Processes for Bayesian Neural Networks Ethan Goan Dimitri Perrin Kerrie Mengersen Clinton Fookes 67 0 0 17 Feb 2023
Improved Langevin Monte Carlo for stochastic optimization via landscape modification Michael C. H. Choi Youjia Wang 29 2 0 08 Feb 2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case Tim Johnston Iosif Lytras Sotirios Sabanis 79 9 0 19 Jan 2023
Geometric ergodicity of SGLD via reflection coupling Lei Li Jian‐Guo Liu Yuliang Wang 92 2 0 17 Jan 2023
$Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient$ Langevin dynamics based algorithm e-TH $\varepsilon$ O POULA for stochastic optimization problems with discontinuous stochastic gradient Dongjae Lim Ariel Neufeld Sotirios Sabanis Ying Zhang 66 7 0 24 Oct 2022
Data Subsampling for Bayesian Neural Networks Eiji Kawasaki M. Holzmann BDL 87 1 0 17 Oct 2022
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics Jeffrey Negrea Jun Yang Haoyue Feng Daniel M. Roy Jonathan H. Huggins 53 1 0 25 Jul 2022
A sharp uniform-in-time error estimate for Stochastic Gradient Langevin Dynamics Lei Li Yuliang Wang 95 11 0 19 Jul 2022
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study Tom Huix Szymon Majewski Alain Durmus Eric Moulines Anna Korba BDL 52 6 0 08 Jul 2022
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo Krishnakumar Balasubramanian Sinho Chewi Murat A. Erdogdu Adil Salim Matthew Shunshi Zhang 108 65 0 10 Feb 2022
Posterior temperature optimized Bayesian models for inverse problems in medical imaging M. Laves Malte Tolle Alexander Schlaefer Sandy Engelhardt 76 10 0 02 Feb 2022
Global convergence of optimized adaptive importance samplers Ömer Deniz Akyildiz 103 7 0 02 Jan 2022
Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials D. Nguyen 58 5 0 17 Dec 2021
Time-independent Generalization Bounds for SGLD in Non-convex Settings Tyler Farghly Patrick Rebeschini 99 24 0 25 Nov 2021
Unsupervised PET Reconstruction from a Bayesian Perspective Chenyu Shen Wenjun Xia H. Ye Mingzheng Hou Hu Chen Yan Liu Jiliu Zhou Yi Zhang 137 3 0 29 Oct 2021
Deep Bayesian inference for seismic imaging with tasks Ali Siahkoohi G. Rizzuti Felix J. Herrmann BDL UQCV 97 21 0 10 Oct 2021
Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics Benjamin J. Zhang Youssef M. Marzouk K. Spiliopoulos 46 8 0 18 Aug 2021
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function Dongjae Lim Ariel Neufeld Sotirios Sabanis Ying Zhang 80 20 0 19 Jul 2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs Vincent Plassier Maxime Vono Alain Durmus Eric Moulines 70 17 0 11 Jun 2021
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning Maxime Vono Vincent Plassier Alain Durmus Aymeric Dieuleveut Eric Moulines FedML 93 36 0 01 Jun 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks Dong-Young Lim Sotirios Sabanis 97 12 0 28 May 2021
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC Jeremie Coullon Leah F. South Christopher Nemeth 76 12 0 27 May 2021
Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics Inass Sekkat G. Stoltz 62 4 0 21 May 2021
On the stability of the stochastic gradient Langevin algorithm with dependent data stream Miklós Rásonyi Kinga Tikosi 17 1 0 04 May 2021
Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization Zhuoran Yang Yufeng Zhang Yongxin Chen Zhaoran Wang OT 91 5 0 21 Dec 2020
Mixing it up: A general framework for Markovian statistics Niklas Dexheimer Claudia Strauch Lukas Trottner 97 9 0 31 Oct 2020
Variance reduction for dependent sequences with applications to Stochastic Gradient MCMC Denis Belomestny L. Iosipoi Eric Moulines A. Naumov S. Samsonov 72 6 0 16 Aug 2020
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating Sotirios Sabanis Ying Zhang 27 7 0 02 Jul 2020
Is SGD a Bayesian sampler? Well, almost Chris Mingard Guillermo Valle Pérez Joar Skalse A. Louis BDL 77 53 0 26 Jun 2020
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms A. Lovas Iosif Lytras Miklós Rásonyi Sotirios Sabanis 88 26 0 25 Jun 2020
Multi-index Antithetic Stochastic Gradient Algorithm Mateusz B. Majka Marc Sabate Vidales Łukasz Szpruch 85 0 0 10 Jun 2020
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness Murat A. Erdogdu Rasa Hosseinzadeh 88 77 0 27 May 2020
Analysis of Stochastic Gradient Descent in Continuous Time J. Latz 81 41 0 15 Apr 2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization Ömer Deniz Akyildiz Sotirios Sabanis 97 17 0 13 Feb 2020