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A Complete Recipe for Stochastic Gradient MCMC

15 June 2015

Papers citing "A Complete Recipe for Stochastic Gradient MCMC"

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Title
Continuous Policy and Value Iteration for Stochastic Control Problems and Its Convergence Qi Feng Gu Wang 12 0 0 09 Jun 2025
Towards Identifiability of Interventional Stochastic Differential Equations Aaron Zweig Zaikang Lin Elham Azizi David A. Knowles 73 1 0 21 May 2025
JaxSGMC: Modular stochastic gradient MCMC in JAX Stephan Thaler Paul Fuchs Ana Cukarska Julija Zavadlav BDL 215 2 0 16 May 2025
Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring Javier Marín 228 0 0 13 Mar 2025
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 114 0 0 25 Feb 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations Haoyang Zheng Guang Lin AI4CE 91 0 0 01 Feb 2025
A noise-corrected Langevin algorithm and sampling by half-denoising Aapo Hyvärinen DiffM 100 1 0 08 Oct 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 178 2 0 31 May 2024
Power-law Dynamic arising from machine learning Wei Chen Weitao Du Zhi-Ming Ma Qi Meng 23 0 0 16 Jun 2023
Fluctuation without dissipation: Microcanonical Langevin Monte Carlo Jakob Robnik U. Seljak 110 7 0 31 Mar 2023
Piecewise Deterministic Markov Processes for Bayesian Neural Networks Ethan Goan Dimitri Perrin Kerrie Mengersen Clinton Fookes 53 0 0 17 Feb 2023
Transport map unadjusted Langevin algorithms: learning and discretizing perturbed samplers Benjamin J. Zhang Youssef M. Marzouk K. Spiliopoulos 81 0 0 14 Feb 2023
Langevin algorithms for very deep Neural Networks with application to image classification Pierre Bras 51 6 0 27 Dec 2022
Pigeonhole Stochastic Gradient Langevin Dynamics for Large Crossed Mixed Effects Models Xinyu Zhang Cheng Li 69 0 0 18 Dec 2022
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images Matthew D. Hoffman T. Le Pavel Sountsov Christopher Suter Ben Lee Vikash K. Mansinghka Rif A. Saurous BDL 75 14 0 27 Oct 2022
Categorical SDEs with Simplex Diffusion Pierre Harvey Richemond Sander Dieleman Arnaud Doucet DiffM 72 26 0 26 Oct 2022
Generalized Fiducial Inference on Differentiable Manifolds Alexander C. Murph Jan Hannig Jonathan P. Williams 55 3 0 30 Sep 2022
$A Particle-Based Algorithm for Distributional Optimization on \textit{Constrained Domains} via Variational Transport and Mirror Descent$ A Particle-Based Algorithm for Distributional Optimization on \textit{Constrained Domains} via Variational Transport and Mirror Descent Dai Hai Nguyen Tetsuya Sakurai 90 2 0 01 Aug 2022
Enhanced gradient-based MCMC in discrete spaces Benjamin Rhodes Michael U. Gutmann 77 17 0 29 Jul 2022
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics Jeffrey Negrea Jun Yang Haoyue Feng Daniel M. Roy Jonathan H. Huggins 51 1 0 25 Jul 2022
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution Zhenyi Wang Li Shen Le Fang Qiuling Suo Tiehang Duan Mingchen Gao OOD 94 43 0 15 Jul 2022
Discrete Langevin Sampler via Wasserstein Gradient Flow Haoran Sun H. Dai Bo Dai Haomin Zhou Dale Schuurmans BDL 90 24 0 29 Jun 2022
Low-Precision Stochastic Gradient Langevin Dynamics Ruqi Zhang A. Wilson Chris De Sa BDL 58 14 0 20 Jun 2022
Bayesian Learning of Parameterised Quantum Circuits Samuel Duffield Marcello Benedetti Matthias Rosenkranz 60 11 0 15 Jun 2022
A stochastic Stein Variational Newton method Alex Leviyev Joshua Chen Yifei Wang Omar Ghattas A. Zimmerman 52 9 0 19 Apr 2022
Distributed data analytics Richard Mortier Hamed Haddadi S. S. Rodríguez Liang Wang 67 2 0 26 Mar 2022
Knowledge Removal in Sampling-based Bayesian Inference Shaopeng Fu Fengxiang He Dacheng Tao BDL MU 81 28 0 24 Mar 2022
Generative Flow Networks for Discrete Probabilistic Modeling Dinghuai Zhang Nikolay Malkin Ziqiang Liu Alexandra Volokhova Aaron Courville Yoshua Bengio 80 110 0 03 Feb 2022
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy Cole Hawkins Alec Koppel Zheng Zhang 73 4 0 18 Jan 2022
Surrogate Likelihoods for Variational Annealed Importance Sampling M. Jankowiak Du Phan BDL 79 13 0 22 Dec 2021
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework Chao Zhang Zhijian Li Hui Qian Xin Du 71 10 0 02 Dec 2021
A Mathematical Walkthrough and Discussion of the Free Energy Principle Beren Millidge A. Seth Christopher L. Buckley 49 9 0 30 Aug 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise Guodong Zhang Kyle Hsu Jianing Li Chelsea Finn Roger C. Grosse 85 40 0 21 Jul 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 242 1,174 0 07 Jul 2021
Applications of the Free Energy Principle to Machine Learning and Neuroscience Beren Millidge DRL 112 8 0 30 Jun 2021
Sampling with Mirrored Stein Operators Jiaxin Shi Chang-rui Liu Lester W. Mackey 128 19 0 23 Jun 2021
Confidence-Aware Learning for Camouflaged Object Detection Jiawei Liu Jing Zhang Nick Barnes 64 13 0 22 Jun 2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci Kevin Roth Gregor Bachmann Sebastian Nowozin Thomas Hofmann CML 77 26 0 11 Jun 2021
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC Jeremie Coullon Leah F. South Christopher Nemeth 76 12 0 27 May 2021
Quantifying Uncertainty in Deep Spatiotemporal Forecasting Dongxian Wu Liyao (Mars) Gao X. Xiong Matteo Chinazzi Alessandro Vespignani Yi-An Ma Rose Yu AI4TS 89 71 0 25 May 2021
A Unifying and Canonical Description of Measure-Preserving Diffusions Alessandro Barp So Takao M. Betancourt Alexis Arnaudon Mark Girolami 72 17 0 06 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 81 389 0 29 Apr 2021
Robust Generalised Bayesian Inference for Intractable Likelihoods Takuo Matsubara Jeremias Knoblauch François‐Xavier Briol Chris J. Oates UQCV 80 80 0 15 Apr 2021
Stein Variational Gradient Descent: many-particle and long-time asymptotics Nikolas Nusken D. M. Renger 99 22 0 25 Feb 2021
Variational Inference for Shrinkage Priors: The R package vir Suchit Mehrotra A. Maity BDL 38 1 0 17 Feb 2021
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting Dongxian Wu Liyao (Mars) Gao X. Xiong Matteo Chinazzi Alessandro Vespignani Yi-An Ma Rose Yu FedML 75 27 0 12 Feb 2021
Exact Langevin Dynamics with Stochastic Gradients Adrià Garriga-Alonso Vincent Fortuin BDL 72 33 0 02 Feb 2021
Bayesian Inference Forgetting Shaopeng Fu Fengxiang He Yue Xu Dacheng Tao MU 78 12 0 16 Jan 2021
The shifted ODE method for underdamped Langevin MCMC James Foster Terry Lyons Harald Oberhauser 91 16 0 10 Jan 2021
Unadjusted Langevin algorithm with multiplicative noise: Total variation and Wasserstein bounds Gilles Pagès Fabien Panloup 34 20 0 28 Dec 2020