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High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm

5 May 2016

Papers citing "High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm"

50 / 145 papers shown

Title
An Introduction to Hamiltonian Monte Carlo Method for Sampling Nisheeth K. Vishnoi 65 14 0 27 Aug 2021
Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension Alain Durmus A. Eberle 81 21 0 02 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
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev Adam D. Cobb T. Javidi Brian Jalaian 88 4 0 15 Jul 2021
Convergence Analysis of Schr{ö}dinger-F{ö}llmer Sampler without Convexity Yuling Jiao Lican Kang Yanyan Liu Youzhou Zhou OT 50 6 0 10 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
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 94 27 0 21 Jun 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
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions Y. Lee Ruoqi Shen Kevin Tian 54 20 0 10 Jun 2021
On Irreversible Metropolis Sampling Related to Langevin Dynamics Zexi Song Z. Tan 31 2 0 06 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
On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems Jan Bohr Richard Nickl 57 17 0 17 May 2021
Mixing Time Guarantees for Unadjusted Hamiltonian Monte Carlo Nawaf Bou-Rabee A. Eberle 110 31 0 03 May 2021
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations J. Sanz-Serna K. Zygalakis 71 23 0 26 Apr 2021
Discrete sticky couplings of functional autoregressive processes Alain Durmus A. Eberle Aurélien Enfroy Arnaud Guillin Pierre Monmarché 46 7 0 14 Apr 2021
Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-Differentiable Priors Jacob Vorstrup Goldman Torben Sell Sumeetpal S. Singh 48 8 0 16 Mar 2021
Decentralized Langevin Dynamics over a Directed Graph Alexander Kolesov Vyacheslav Kungurtsev 63 2 0 06 Mar 2021
Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems Tiangang Cui O. Zahm 63 25 0 26 Feb 2021
The Langevin Monte Carlo algorithm in the non-smooth log-concave case Joseph Lehec 76 30 0 26 Jan 2021
Unadjusted Langevin algorithm with multiplicative noise: Total variation and Wasserstein bounds Gilles Pagès Fabien Panloup 41 20 0 28 Dec 2020
Complexity of zigzag sampling algorithm for strongly log-concave distributions Jianfeng Lu Lihan Wang 65 6 0 21 Dec 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method Ye He Krishnakumar Balasubramanian Murat A. Erdogdu 66 35 0 06 Nov 2020
Mixing it up: A general framework for Markovian statistics Niklas Dexheimer Claudia Strauch Lukas Trottner 97 9 0 31 Oct 2020
Efficient constrained sampling via the mirror-Langevin algorithm Kwangjun Ahn Sinho Chewi 117 57 0 30 Oct 2020
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC Arun Ganesh Kunal Talwar FedML 84 41 0 27 Oct 2020
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev Tianlin Li Qi Lei Ioannis Panageas 65 20 0 11 Oct 2020
On the cost of Bayesian posterior mean strategy for log-concave models S. Gadat Fabien Panloup Clément Pellegrini 59 7 0 08 Oct 2020
Structured Logconcave Sampling with a Restricted Gaussian Oracle Y. Lee Ruoqi Shen Kevin Tian 79 73 0 07 Oct 2020
On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms Richard Nickl Sven Wang 68 41 0 11 Sep 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
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis Valentin De Bortoli Alain Durmus A. F. Vidal Marcelo Pereyra 83 20 0 13 Aug 2020
Ergodicity of the underdamped mean-field Langevin dynamics A. Kazeykina Zhenjie Ren Xiaolu Tan Junjian Yang 80 16 0 29 Jul 2020
High-dimensional MCMC with a standard splitting scheme for the underdamped Langevin diffusion Pierre Monmarché 102 47 0 10 Jul 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
A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba Adil Salim Michael Arbel Giulia Luise Arthur Gretton 84 78 0 17 Jun 2020
Composite Logconcave Sampling with a Restricted Gaussian Oracle Ruoqi Shen Kevin Tian Y. Lee 66 10 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
On the limitations of single-step drift and minorization in Markov chain convergence analysis Qian Qin J. Hobert 51 32 0 21 Mar 2020
Central limit theorems for Markov chains based on their convergence rates in Wasserstein distance Rui Jin Aixin Tan 66 6 0 21 Feb 2020
Fast Convergence for Langevin Diffusion with Manifold Structure Ankur Moitra Andrej Risteski 66 7 0 13 Feb 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
Wasserstein Control of Mirror Langevin Monte Carlo Kelvin Shuangjian Zhang Gabriel Peyré M. Fadili Marcelo Pereyra 71 66 0 11 Feb 2020
Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo Y. Lee Ruoqi Shen Kevin Tian 84 37 0 10 Feb 2020
The reproducing Stein kernel approach for post-hoc corrected sampling Liam Hodgkinson R. Salomone Fred Roosta 72 27 0 25 Jan 2020
Aggregated Gradient Langevin Dynamics Chao Zhang Jiahao Xie Zebang Shen P. Zhao Tengfei Zhou Hui Qian 81 1 0 21 Oct 2019
Validated Variational Inference via Practical Posterior Error Bounds Jonathan H. Huggins Mikolaj Kasprzak Trevor Campbell Tamara Broderick 90 37 0 09 Oct 2019
Variance reduction for Markov chains with application to MCMC Denis Belomestny L. Iosipoi Eric Moulines A. Naumov S. Samsonov BDL 75 30 0 08 Oct 2019
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization Ying Zhang Ömer Deniz Akyildiz Theodoros Damoulas Sotirios Sabanis 104 47 0 04 Oct 2019
The Randomized Midpoint Method for Log-Concave Sampling Ruoqi Shen Y. Lee 125 118 0 12 Sep 2019
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm Wenlong Mou Yian Ma Martin J. Wainwright Peter L. Bartlett Michael I. Jordan DiffM 84 85 0 28 Aug 2019