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Consistency and fluctuations for stochastic gradient Langevin dynamics

1 September 2014

Yee Whye Teh

Alexandre Hoang Thiery

Sebastian J. Vollmer

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Papers citing "Consistency and fluctuations for stochastic gradient Langevin dynamics"

47 / 147 papers shown

Title
Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time Hejian Sang Jia-Wei Liu ODL 112 1 0 23 May 2018
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning Yichi Zhang Zhijian Ou 62 0 0 01 Mar 2018
Stochastic quasi-Newton with adaptive step lengths for large-scale problems A. Wills Thomas B. Schon 63 9 0 12 Feb 2018
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors Gintare Karolina Dziugaite Daniel M. Roy MLT 92 145 0 26 Dec 2017
On Connecting Stochastic Gradient MCMC and Differential Privacy Bai Li Changyou Chen Hao Liu Lawrence Carin 76 38 0 25 Dec 2017
Natural Langevin Dynamics for Neural Networks Gaétan Marceau-Caron Yann Ollivier BDL 85 31 0 04 Dec 2017
Particle Optimization in Stochastic Gradient MCMC Changyou Chen Ruiyi Zhang 65 10 0 29 Nov 2017
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo Jack Baker Paul Fearnhead E. Fox Christopher Nemeth BDL 84 11 0 02 Oct 2017
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient A. Dalalyan Avetik G. Karagulyan 144 298 0 29 Sep 2017
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference Jonathan H. Huggins Ryan P. Adams Tamara Broderick 109 33 0 26 Sep 2017
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC Changyou Chen Wenlin Wang Yizhe Zhang Qinliang Su Lawrence Carin 100 28 0 04 Sep 2017
Continuous-Time Flows for Efficient Inference and Density Estimation Changyou Chen Chunyuan Li Liquan Chen Wenlin Wang Yunchen Pu Lawrence Carin TPM 129 57 0 04 Sep 2017
Hamiltonian Monte Carlo with Energy Conserving Subsampling Khue-Dung Dang M. Quiroz Robert Kohn Minh-Ngoc Tran M. Villani 120 62 0 02 Aug 2017
Mini-batch Tempered MCMC Dangna Li W. Wong 92 6 0 31 Jul 2017
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI Justin Domke BDL 80 6 0 20 Jun 2017
Control Variates for Stochastic Gradient MCMC Jack Baker Paul Fearnhead E. Fox Christopher Nemeth BDL 102 101 0 16 Jun 2017
Stochastic Gradient Monomial Gamma Sampler Yizhe Zhang Changyou Chen Zhe Gan Ricardo Henao Lawrence Carin BDL 81 11 0 05 Jun 2017
Geometry and Dynamics for Markov Chain Monte Carlo Alessandro Barp François‐Xavier Briol A. Kennedy Mark Girolami AI4CE 88 31 0 08 May 2017
Efficient variational Bayesian neural network ensembles for outlier detection Nick Pawlowski Miguel Jaques Ben Glocker BDL UQCV 57 13 0 20 Mar 2017
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics Yuchen Zhang Percy Liang Moses Charikar 110 236 0 18 Feb 2017
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling Zhe Gan Chunyuan Li Changyou Chen Yunchen Pu Qinliang Su Lawrence Carin BDL UQCV 139 41 0 23 Nov 2016
Measuring Sample Quality with Diffusions Jackson Gorham Andrew B. Duncan Sandra Jeanne Vollmer Lester W. Mackey 172 117 0 21 Nov 2016
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen Nan Ding Lawrence Carin 93 163 0 21 Oct 2016
Black-box Importance Sampling Qiang Liu Jason D. Lee FAtt 79 74 0 17 Oct 2016
Multilevel Monte Carlo for Scalable Bayesian Computations M. Giles Tigran Nagapetyan Lukasz Szpruch Sebastian J. Vollmer K. Zygalakis 79 9 0 15 Sep 2016
Quasi-stationary Monte Carlo and the ScaLE Algorithm M. Pollock Paul Fearnhead A. M. Johansen Gareth O. Roberts 107 18 0 12 Sep 2016
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data J. Bierkens Paul Fearnhead Gareth O. Roberts 96 233 0 11 Jul 2016
Merging MCMC Subposteriors through Gaussian-Process Approximations Christopher Nemeth Chris Sherlock 99 50 0 27 May 2016
Coresets for Scalable Bayesian Logistic Regression Jonathan H. Huggins Trevor Campbell Tamara Broderick 101 219 0 20 May 2016
Quantifying the accuracy of approximate diffusions and Markov chains Jonathan H. Huggins James Zou 132 29 0 20 May 2016
Patterns of Scalable Bayesian Inference E. Angelino Matthew J. Johnson Ryan P. Adams 114 87 0 16 Feb 2016
Stochastic Quasi-Newton Langevin Monte Carlo Umut Simsekli Roland Badeau A. Cemgil G. Richard BDL 72 62 0 10 Feb 2016
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server Leonard Hasenclever Stefan Webb Thibaut Lienart Sebastian J. Vollmer Balaji Lakshminarayanan Charles Blundell Yee Whye Teh BDL 199 70 0 31 Dec 2015
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization Changyou Chen David Carlson Zhe Gan Chunyuan Li Lawrence Carin 98 90 0 25 Dec 2015
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks Chunyuan Li Changyou Chen David Carlson Lawrence Carin ODL BDL 119 327 0 23 Dec 2015
Guaranteed inference in topic models Khoat Than Tung Doan 37 8 0 10 Dec 2015
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method Alexandre Bouchard-Côté Sebastian J. Vollmer Arnaud Doucet 140 237 0 08 Oct 2015
Provable Bayesian Inference via Particle Mirror Descent Bo Dai Niao He H. Dai Le Song 159 73 0 09 Jun 2015
Parallel Stochastic Gradient Markov Chain Monte Carlo for Matrix Factorisation Models Umut Simsekli Hazal Koptagel Hakan Güldaş taylan. cemgil Figen Oztoprak Ilker Birbil 117 12 0 03 Jun 2015
On Markov chain Monte Carlo methods for tall data Rémi Bardenet Arnaud Doucet Chris Holmes 76 279 0 11 May 2015
Fast Differentially Private Matrix Factorization Ziqi Liu Yu Wang Alex Smola FedML 103 124 0 06 May 2015
Fast Sampling for Bayesian Max-Margin Models Wenbo Hu Jun Zhu Bo Zhang BDL 57 0 0 27 Apr 2015
Perturbation theory for Markov chains via Wasserstein distance Daniel Rudolf Nikolaus Schweizer 140 108 0 13 Mar 2015
Bayesian computation: a perspective on the current state, and sampling backwards and forwards P. Green K. Latuszyñski Marcelo Pereyra Christian P. Robert 135 21 0 04 Feb 2015
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) Maurizio Filippone Raphael Engler 116 31 0 22 Jan 2015
(Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics Sebastian J. Vollmer K. Zygalakis and Yee Whye Teh 104 49 0 02 Jan 2015
Big Learning with Bayesian Methods Jun Zhu Jianfei Chen Wenbo Hu Bo Zhang BDL 544 84 0 24 Nov 2014